Exercise 1
Lan Phan & Angelica Leon
9/25/2022
Wickham & Grolemund (2017)
Chapter 3
3.2.4 Exercises
- Run ggplot(data = mpg). What do you see?
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.2## Warning: replacing previous import 'ellipsis::check_dots_unnamed' by
## 'rlang::check_dots_unnamed' when loading 'tibble'## Warning: replacing previous import 'ellipsis::check_dots_used' by
## 'rlang::check_dots_used' when loading 'tibble'## Warning: replacing previous import 'ellipsis::check_dots_empty' by
## 'rlang::check_dots_empty' when loading 'tibble'## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✔ ggplot2 3.3.5 ✔ purrr 0.3.4
## ✔ tibble 3.0.4 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.0
## ✔ readr 1.4.0 ✔ forcats 0.5.0## Warning: package 'ggplot2' was built under R version 4.0.2## Warning: package 'tibble' was built under R version 4.0.2## Warning: package 'readr' was built under R version 4.0.2## Warning: package 'purrr' was built under R version 4.0.2## Warning: package 'stringr' was built under R version 4.0.2## Warning: package 'forcats' was built under R version 4.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()ggplot(data = mpg)
This code outputs an empty plot.
- How many rows are in mpg? How many columns?
mpg## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rowsnrow(mpg)## [1] 234ncol(mpg)## [1] 11There are 234 rows and 11 columns. We also know this from the 234 × 11 tibble description.
- What does the drv variable describe? Read the help for ?mpg to find out.
?mpgThe drv variable indicates the type of drive train, where f = front-wheel drive, r = rear wheel drive, and 4 = 4wd.
- Make a scatterplot of hwy vs cyl.
ggplot(data = mpg) +
geom_point(mapping = aes(x = cyl, y = hwy))
- What happens if you make a scatterplot of class vs drv? Why is the plot not useful?
ggplot(data = mpg) +
geom_point(mapping = aes(x = drv, y = class))
This plot isn’t useful because both variables are categorical, where class has 7 categories and drv only has 3 categories represented in this graph. Since there are limited combinations of both variables revealed through this plot, no discernible pattern can be identified.
3.3.1 Exercises
- What’s gone wrong with this code? Why are the points not blue?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
The points are not blue because there is a missing close parentheses after the “aes” function. The “color=”blue” code is being read as part of the aes function, rather than part of the mapping function. To correct, the code should read as:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
- Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?
?mpg
mpg## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rowsThe following variables are categorical: manufacturer, model, trans,
drv, fl, class. The following variables are continuous: displ, year,
cyl, cty, hwy. We know this based on the classification type under each
variable name when mpg is ran. The categorical variables are indicated
by
- Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = displ))
When mapping a cont. variable to color, the scale appears in a gradient (defaulted to blue), where the colors of the gradient represent values of the cont. variable.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, size = displ))
When mapping a cont. variable to size, we see the values of the variable represented through shape size. Here, larger values are represented as larger circles and smaller values are represented by smaller circles.
We get an error message when we try to map a continuous variable to shape. The code was not included so we could generate this html.
- What happens if you map the same variable to multiple aesthetics?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = displ, size = displ))
We see the color and size aesthetics combined in the plot; here, the points reflect both the size scale and color gradient corresponding to the values of the cont. variable.
- What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)
Stroke adjusts the thickness of the border for shapes plotted. It only works for shapes 21-24. The larger the number, the larger in area of the border is. A sample is below:
?geom_point
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), shape = 24,
fill = 'blue', size = 3, stroke = 5, color = 'red')
- What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)? Note, you’ll also need to specify x and y.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = displ < 5))
Using aes(colour = displ < 5) categorizes the displ variable dichotomously into “True” and “False” categories, where values <5 fall under “True” and everything else (values > or = 5) falls under “False”. These two categories are color-coded with different colors (here, blue = true and red = false).
3.5.1 Exercises 1. What happens if you facet on a continuous variable?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~cty)
When you facet on a continuous variable, it facets according to each level of the variable represented in the dataset. I’d imagine this would not be helpful if there were many levels/values of a continuous variable represented in the data set. Categorical variables tend to have fewer levels, and therefore faceting could be more useful.
- What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y= hwy)) +
facet_grid(drv ~ cyl)
The empty grids indicate that there are no observations for that combination of cyl and drv.
- What plots does the following code make? What does . do?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ .)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(. ~ cyl)
The first plot plots hwy vs. displ, where the rows are facetted by drv. The second plot plots hwy vs. displ, where the columns are facetted by cyl.
- Take the first faceted plot in this section. What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2)
A main advantage of using facetting instead of coloring is it allows for more direct interpretation of the plot based on the facetted variable, rather than having to refer back and forth to the legend vs. plot, and having to mentally keep track of what each color represents. It also minimizes the noise/busyness of having many colors on one plot.
Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol arguments? For the function facet_wrap(), nrow and ncol allow you to indicate the number of rows and columns. facet_grid() does not have nrow and ncol since the number of rows and columns are set by to the number of unique levels in the row/column variables.
When using facet_grid() you should usually put the variable with more unique levels in the columns. Why? When facetting a plot, it is usually easier to make comparisons of plots when they are side by side (i.e., facetted by columns), rather than making comparisons of plots that are above and below each other. The fact that the DV typically sits on the y-axis contributes to why the former is easier for interpretation than the latter.
3.6.1 Exercises
What geom would you use to draw a line chart? A boxplot? A histogram? An area chart? I’d use the following to draw each respectively: geom_line(), geom_boxplot(), geom_histogram(), and geom_area().
Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.
I predicted this graph would illustrate hwy vs. displ, where the points are color-coded by the categories of drv. geom_smooth() will draw a different line, with a different linetype, for each unique value of the variable that’s mapped, so we should see 3 unique lines representing the levels of drv, each abiding to the color-coding scheme.
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_point() +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
- What does show.legend = FALSE do? What happens if you remove it? Why do you think I used it earlier in the chapter?
show.legend = FALSE hides the legend on the right hand side of the plot, when a variable is mapped to an aesthetic. show.legend is defaulted to = TRUE. It was used earlier in the chapter to hide the legend of the example plot.
- What does the se argument to geom_smooth() do?
?geom_smoothThe se argument displays the confidence interval around smooth (TRUE by default).
- Will these two graphs look different? Why/why not?
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot() +
geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
No, these graphs will look identical because they are asking for the same output, just written in different ways. In the first code, the plot is mapped and then “geomed”. In the second code, the plots are mapped and defined within each geom.
- Recreate the R code necessary to generate the following graphs.
ggplot(data = mpg, mapping = aes(y = hwy, x = displ)) +
geom_point() +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(data = mpg, mapping = aes(y = hwy, x = displ)) +
geom_point() +
geom_smooth(mapping = aes(group = drv), se = FALSE, show.legend = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(data = mpg, mapping = aes(y = hwy, x = displ)) +
geom_point(mapping = aes(color = drv)) +
geom_smooth(mapping = aes(color = drv, group = drv), se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(data = mpg, mapping = aes(y = hwy, x = displ)) +
geom_point(mapping = aes(color = drv)) +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(data = mpg, mapping = aes(y = hwy, x = displ)) +
geom_point(mapping = aes(color = drv)) +
geom_smooth(mapping = aes(linetype = drv), se = FALSE)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(data = mpg, mapping = aes(y = hwy, x = displ)) +
geom_point(mapping = aes(fill = drv), color = 'white', stroke = 3, shape = 21, size = 4)
3.7.1 Exercises
1.What is the default geom associated with stat_summary()? How could you rewrite the previous plot to use that geom function instead of the stat function?
The default geom associated with stat_summary() is geom_pointrange(). The previous plot can be rewritten with the following code:
diamonds %>% group_by(cut) %>% summarize(median_y = median(depth),
min_y = min(depth),
max_y = max(depth)) %>%
ggplot() +
geom_pointrange(mapping = aes(x = cut, y = median_y, ymin = min_y, ymax = max_y)) +
labs(y = 'depth')
- What does geom_col() do? How is it different to geom_bar()?
geom_col() and geom_bar() create bar charts. geom_bar() makes the height of the bar proportional to the number of cases in each group. On the other hand, geom_col() makes the heights of the bars to represent values in the data. The heights of the bars represent the values in the data. geom_bar() uses stat_count() by default and counts the number of cases at each x position, while geom_col() uses stat_identity() and leaves the data as is.
- Most geoms and stats come in pairs that are almost always used in concert. Read through the documentation and make a list of all the pairs. What do they have in common?
Geom & Stats Pairs: geom_bar() & stat_count() geom_bin2d() & stat_bin_2d() geom_boxplot() & stat_boxplot() geom_contour_filled() & stat_contour_filled() geom_contour() & stat_contour() geom_count() & stat_sum() geom_density_2d() & stat_density_2d() geom_density() & stat_density() geom_dotplot() & stat_bindot() geom_function() & stat_function() geom_sf() & stat_sf() geom_sf() & stat_sf() geom_smooth() & stat_smooth() geom_violin() & stat_ydensity() geom_hex() & stat_bin_hex() geom_qq_line() & stat_qq_line() geom_qq() & stat_qq() geom_quantile() & stat_quantile() The names of the geoms and stats pairs tend to be the same (e.g., geom_boxplot() and stat_boxplot()).
4.What variables does stat_smooth() compute? What parameters control its behaviour?
stat_smooth() calculates the following variables: y or x : predicted value ymin or xmin: lower pointwise confidence interval around the mean ymax or xmax : upper pointwise confidence interval around the mean se: standard error
The following parameters control the behavior: method formula se na.rm method.args (see ?stat_smooth() for list under arguments)
- In our proportion bar chart, we need to set group = 1. Why? In other words what is the problem with these two graphs?
In the proportion bar chart, the bars in the plot will have the same height ( a height of 1), if the group is not set to group = 1. Thus, the problem with the two graphs is that the plots have the same height, since geom_bar() makes the height of the bar proportional to the number of cases in each group. Setting group=1 will correct for this.
3.8.1 Exercises 1. What is the problem with this plot? How could you improve it?
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point()
In this plot, many points of hwy and cty overlap each other. This problem is known as overplotting. You can use a jitter position adjustment to decrease overplotting. position = “jitter” adds a small amount of random noise to each point and therefore spreads the points out more.
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point(position = "jitter")
2.What parameters to geom_jitter() control the amount of jittering?
There are two arguments to jitter that control the amount of jittering: width, which controls the amount of horizontal displacement height, which controls the amount of vertical displacement The default introduces noise in both directions.
- Compare and contrast geom_jitter() with geom_count().
geom_jitter() adds a small amount of random noise to each point to reduce overplotting. Thus, the locations of x and y are changed slightly. geom_count() does not change the location of x and y. If there are many points on one location, then the size of the points will increase. In other words, values with more observations will be larger than those with fewer observations.
4.What’s the default position adjustment for geom_boxplot()? Create a visualisation of the mpg dataset that demonstrates it.
The default position adjustment for geom_boxplot() is “dodge2”. You can see in the below that there are multiple boxplots for each level fo the DV (drv), and dodge2 positions the boxplots so you can see each and they are not overlapping.
ggplot(data = mpg, aes(x = hwy, y = drv, color = class)) +
geom_boxplot()
3.9.1 Exercises 1.Turn a stacked bar chart into a pie chart using coord_polar().
ggplot(data = mpg) +
geom_bar(mapping = aes(x = drv, fill = manufacturer))+
coord_polar()
2.What does labs() do? Read the documentation.
labs() lets you edit the axis, plot, and legend labels.
3.What’s the difference between coord_quickmap() and coord_map()?
The coord_quickmap() function projects a portion of the earth, which is approximately spherical, onto a flat 2D plane. This function does not preserve straight lines. On the other hand, coord_quickmap() is a quick approximation that does preserve straight lines.
4.What does the plot below tell you about the relationship between city and highway mpg? Why is coord_fixed() important? What does geom_abline() do?
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point() +
geom_abline() +
coord_fixed()
The plot indicates a positive linear relationship between hwy and cty (as cty increases, hwy increases). geom_abline() adds the diagonal line to the plot, with a slope of 1. This allows for us to gauge the approximate slope of the relationship between hwy and cty, according to this reference line; the slope of the positive linear relationship between hwy and cty is close to 1 (a unit increase in cty is associated with a unit increase in hwy).
coord_fixed forces a specified ratio between the physical representation of data units on the axes.The ratio represents the number of units on the y-axis equivalent to one unit on the x-axis. The default is ratio = 1 and ensures that one unit on the x-axis is the same length as one unit on the y-axis. Here, it allows us to more accurately reference the line from geom_abline() and infer the slope of 1.
Chapter 4
4.4 Exercises 1. Why does this code not work?
The code doesn’t work because in the second line, there is a typo. “my_varıable” needs to be corrected to “my_variable” to run seamlessly. See the corrected code below:
my_variable <- 10
my_variable## [1] 10- Tweak each of the following R commands so that they run correctly: See corrections below:
library(tidyverse)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
filter(mpg, cyl == 8)## # A tibble: 70 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a6 quattro 4.2 2008 8 auto… 4 16 23 p mids…
## 2 chevrolet c1500 sub… 5.3 2008 8 auto… r 14 20 r suv
## 3 chevrolet c1500 sub… 5.3 2008 8 auto… r 11 15 e suv
## 4 chevrolet c1500 sub… 5.3 2008 8 auto… r 14 20 r suv
## 5 chevrolet c1500 sub… 5.7 1999 8 auto… r 13 17 r suv
## 6 chevrolet c1500 sub… 6 2008 8 auto… r 12 17 r suv
## 7 chevrolet corvette 5.7 1999 8 manu… r 16 26 p 2sea…
## 8 chevrolet corvette 5.7 1999 8 auto… r 15 23 p 2sea…
## 9 chevrolet corvette 6.2 2008 8 manu… r 16 26 p 2sea…
## 10 chevrolet corvette 6.2 2008 8 auto… r 15 25 p 2sea…
## # … with 60 more rowsfilter(diamonds, carat > 3)## # A tibble: 32 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 3.01 Premium I I1 62.7 58 8040 9.1 8.97 5.67
## 2 3.11 Fair J I1 65.9 57 9823 9.15 9.02 5.98
## 3 3.01 Premium F I1 62.2 56 9925 9.24 9.13 5.73
## 4 3.05 Premium E I1 60.9 58 10453 9.26 9.25 5.66
## 5 3.02 Fair I I1 65.2 56 10577 9.11 9.02 5.91
## 6 3.01 Fair H I1 56.1 62 10761 9.54 9.38 5.31
## 7 3.65 Fair H I1 67.1 53 11668 9.53 9.48 6.38
## 8 3.24 Premium H I1 62.1 58 12300 9.44 9.4 5.85
## 9 3.22 Ideal I I1 62.6 55 12545 9.49 9.42 5.92
## 10 3.5 Ideal H I1 62.8 57 12587 9.65 9.59 6.03
## # … with 22 more rows- Press Alt + Shift + K. What happens? How can you get to the same place using the menus?
The Keyboard Shortcut Quick Reference appears. Using the menus, click Help, then Keyboard Shortcuts Help.
##Chapter 5
Install data
library(nycflights13)## Warning: package 'nycflights13' was built under R version 4.0.2colnames(flights)## [1] "year" "month" "day" "dep_time"
## [5] "sched_dep_time" "dep_delay" "arr_time" "sched_arr_time"
## [9] "arr_delay" "carrier" "flight" "tailnum"
## [13] "origin" "dest" "air_time" "distance"
## [17] "hour" "minute" "time_hour"5.2.4 Exercises
- Find all flights that
#1. Had an arrival delay of two or more hours
filter(flights, arr_delay >= 120)## # A tibble: 10,200 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 811 630 101 1047 830 137 MQ
## 2 2013 1 1 848 1835 853 1001 1950 851 MQ
## 3 2013 1 1 957 733 144 1056 853 123 UA
## 4 2013 1 1 1114 900 134 1447 1222 145 UA
## 5 2013 1 1 1505 1310 115 1638 1431 127 EV
## 6 2013 1 1 1525 1340 105 1831 1626 125 B6
## 7 2013 1 1 1549 1445 64 1912 1656 136 EV
## 8 2013 1 1 1558 1359 119 1718 1515 123 EV
## 9 2013 1 1 1732 1630 62 2028 1825 123 EV
## 10 2013 1 1 1803 1620 103 2008 1750 138 MQ
## # … with 10,190 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#2 Flew to Houston (IAH or HOU)
filter(flights, dest == "IAH" | dest == "HOU")## # A tibble: 9,313 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 517 515 2 830 819 11 UA
## 2 2013 1 1 533 529 4 850 830 20 UA
## 3 2013 1 1 623 627 -4 933 932 1 UA
## 4 2013 1 1 728 732 -4 1041 1038 3 UA
## 5 2013 1 1 739 739 0 1104 1038 26 UA
## 6 2013 1 1 908 908 0 1228 1219 9 UA
## 7 2013 1 1 1028 1026 2 1350 1339 11 UA
## 8 2013 1 1 1044 1045 -1 1352 1351 1 UA
## 9 2013 1 1 1114 900 134 1447 1222 145 UA
## 10 2013 1 1 1205 1200 5 1503 1505 -2 UA
## # … with 9,303 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#3 Were operated by United, American, or Delta
filter(flights, carrier %in% c("UA", "AA", "DL"))## # A tibble: 139,504 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 517 515 2 830 819 11 UA
## 2 2013 1 1 533 529 4 850 830 20 UA
## 3 2013 1 1 542 540 2 923 850 33 AA
## 4 2013 1 1 554 600 -6 812 837 -25 DL
## 5 2013 1 1 554 558 -4 740 728 12 UA
## 6 2013 1 1 558 600 -2 753 745 8 AA
## 7 2013 1 1 558 600 -2 924 917 7 UA
## 8 2013 1 1 558 600 -2 923 937 -14 UA
## 9 2013 1 1 559 600 -1 941 910 31 AA
## 10 2013 1 1 559 600 -1 854 902 -8 UA
## # … with 139,494 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#4 Departed in summer (July, August, and September)
filter(flights, month %in% 7:9)## # A tibble: 86,326 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 7 1 1 2029 212 236 2359 157 B6
## 2 2013 7 1 2 2359 3 344 344 0 B6
## 3 2013 7 1 29 2245 104 151 1 110 B6
## 4 2013 7 1 43 2130 193 322 14 188 B6
## 5 2013 7 1 44 2150 174 300 100 120 AA
## 6 2013 7 1 46 2051 235 304 2358 186 B6
## 7 2013 7 1 48 2001 287 308 2305 243 VX
## 8 2013 7 1 58 2155 183 335 43 172 B6
## 9 2013 7 1 100 2146 194 327 30 177 B6
## 10 2013 7 1 100 2245 135 337 135 122 B6
## # … with 86,316 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#5 Arrived more than two hours late, but didn’t leave late
filter(flights, arr_delay > 120 & dep_delay <= 0)## # A tibble: 29 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 27 1419 1420 -1 1754 1550 124 MQ
## 2 2013 10 7 1350 1350 0 1736 1526 130 EV
## 3 2013 10 7 1357 1359 -2 1858 1654 124 AA
## 4 2013 10 16 657 700 -3 1258 1056 122 B6
## 5 2013 11 1 658 700 -2 1329 1015 194 VX
## 6 2013 3 18 1844 1847 -3 39 2219 140 UA
## 7 2013 4 17 1635 1640 -5 2049 1845 124 MQ
## 8 2013 4 18 558 600 -2 1149 850 179 AA
## 9 2013 4 18 655 700 -5 1213 950 143 AA
## 10 2013 5 22 1827 1830 -3 2217 2010 127 MQ
## # … with 19 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#6 Were delayed by at least an hour, but made up over 30 minutes in flight
filter(flights, dep_delay >= 60 & (dep_delay - arr_delay > 30))## # A tibble: 1,844 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 2205 1720 285 46 2040 246 AA
## 2 2013 1 1 2326 2130 116 131 18 73 B6
## 3 2013 1 3 1503 1221 162 1803 1555 128 UA
## 4 2013 1 3 1839 1700 99 2056 1950 66 AA
## 5 2013 1 3 1850 1745 65 2148 2120 28 AA
## 6 2013 1 3 1941 1759 102 2246 2139 67 UA
## 7 2013 1 3 1950 1845 65 2228 2227 1 B6
## 8 2013 1 3 2015 1915 60 2135 2111 24 9E
## 9 2013 1 3 2257 2000 177 45 2224 141 9E
## 10 2013 1 4 1917 1700 137 2135 1950 105 AA
## # … with 1,834 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#7 Departed between midnight and 6am (inclusive)
filter(flights, dep_time >= 0000 & dep_time <= 0600)## # A tibble: 9,344 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 517 515 2 830 819 11 UA
## 2 2013 1 1 533 529 4 850 830 20 UA
## 3 2013 1 1 542 540 2 923 850 33 AA
## 4 2013 1 1 544 545 -1 1004 1022 -18 B6
## 5 2013 1 1 554 600 -6 812 837 -25 DL
## 6 2013 1 1 554 558 -4 740 728 12 UA
## 7 2013 1 1 555 600 -5 913 854 19 B6
## 8 2013 1 1 557 600 -3 709 723 -14 EV
## 9 2013 1 1 557 600 -3 838 846 -8 B6
## 10 2013 1 1 558 600 -2 753 745 8 AA
## # … with 9,334 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay- Another useful dplyr filtering helper is between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?
Between indicates the lower and upper cut of the values we are looking for. For example, we can say x is between 7 and 9 if 7<x<9 See below for the simplified code
filter(flights, between(month, 7, 9))## # A tibble: 86,326 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 7 1 1 2029 212 236 2359 157 B6
## 2 2013 7 1 2 2359 3 344 344 0 B6
## 3 2013 7 1 29 2245 104 151 1 110 B6
## 4 2013 7 1 43 2130 193 322 14 188 B6
## 5 2013 7 1 44 2150 174 300 100 120 AA
## 6 2013 7 1 46 2051 235 304 2358 186 B6
## 7 2013 7 1 48 2001 287 308 2305 243 VX
## 8 2013 7 1 58 2155 183 335 43 172 B6
## 9 2013 7 1 100 2146 194 327 30 177 B6
## 10 2013 7 1 100 2245 135 337 135 122 B6
## # … with 86,316 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay- How many flights have a missing dep_time? What other variables are missing? What might these rows represent?
summary(flights$dep_time) #8255 missing## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1 907 1401 1349 1744 2400 8255# What other variables are missing
filter(flights, is.na(dep_time))## # A tibble: 8,255 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 NA 1630 NA NA 1815 NA EV
## 2 2013 1 1 NA 1935 NA NA 2240 NA AA
## 3 2013 1 1 NA 1500 NA NA 1825 NA AA
## 4 2013 1 1 NA 600 NA NA 901 NA B6
## 5 2013 1 2 NA 1540 NA NA 1747 NA EV
## 6 2013 1 2 NA 1620 NA NA 1746 NA EV
## 7 2013 1 2 NA 1355 NA NA 1459 NA EV
## 8 2013 1 2 NA 1420 NA NA 1644 NA EV
## 9 2013 1 2 NA 1321 NA NA 1536 NA EV
## 10 2013 1 2 NA 1545 NA NA 1910 NA AA
## # … with 8,245 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#arrival time is also missing
#What might these rows represent?
#these are flights that did not happen/ have not taken place- Why is NA ^ 0 not missing? Because all number ^ 0 equals 1
Why is NA | TRUE not missing? This is a “or” statement, so TRUE can be the value regardless of NA
Why is FALSE & NA not missing? This is an “and” statement, anything with FALSE will be false
Can you figure out the general rule? (NA * 0 is a tricky counterexample!) The general rule is that if a rule applies to all numbers, it will apply to NA The rules for multiplication by 0 is not applicable to infinite numbers
5.3.1 Exercises
- How could you use arrange() to sort all missing values to the start? (Hint: use is.na()).
arrange(flights, desc(is.na(dep_time)), dep_time)## # A tibble: 336,776 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 NA 1630 NA NA 1815 NA EV
## 2 2013 1 1 NA 1935 NA NA 2240 NA AA
## 3 2013 1 1 NA 1500 NA NA 1825 NA AA
## 4 2013 1 1 NA 600 NA NA 901 NA B6
## 5 2013 1 2 NA 1540 NA NA 1747 NA EV
## 6 2013 1 2 NA 1620 NA NA 1746 NA EV
## 7 2013 1 2 NA 1355 NA NA 1459 NA EV
## 8 2013 1 2 NA 1420 NA NA 1644 NA EV
## 9 2013 1 2 NA 1321 NA NA 1536 NA EV
## 10 2013 1 2 NA 1545 NA NA 1910 NA AA
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay- Sort flights to find the most delayed flights
arrange(flights, desc(dep_delay))## # A tibble: 336,776 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 9 641 900 1301 1242 1530 1272 HA
## 2 2013 6 15 1432 1935 1137 1607 2120 1127 MQ
## 3 2013 1 10 1121 1635 1126 1239 1810 1109 MQ
## 4 2013 9 20 1139 1845 1014 1457 2210 1007 AA
## 5 2013 7 22 845 1600 1005 1044 1815 989 MQ
## 6 2013 4 10 1100 1900 960 1342 2211 931 DL
## 7 2013 3 17 2321 810 911 135 1020 915 DL
## 8 2013 6 27 959 1900 899 1236 2226 850 DL
## 9 2013 7 22 2257 759 898 121 1026 895 DL
## 10 2013 12 5 756 1700 896 1058 2020 878 AA
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay#Find the flights that left earliest
arrange(flights, year, month, day, dep_time)## # A tibble: 336,776 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 517 515 2 830 819 11 UA
## 2 2013 1 1 533 529 4 850 830 20 UA
## 3 2013 1 1 542 540 2 923 850 33 AA
## 4 2013 1 1 544 545 -1 1004 1022 -18 B6
## 5 2013 1 1 554 600 -6 812 837 -25 DL
## 6 2013 1 1 554 558 -4 740 728 12 UA
## 7 2013 1 1 555 600 -5 913 854 19 B6
## 8 2013 1 1 557 600 -3 709 723 -14 EV
## 9 2013 1 1 557 600 -3 838 846 -8 B6
## 10 2013 1 1 558 600 -2 753 745 8 AA
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay- Sort flights to find the fastest (highest speed) flights.
arrange(flights, desc(distance/air_time))## # A tibble: 336,776 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 5 25 1709 1700 9 1923 1937 -14 DL
## 2 2013 7 2 1558 1513 45 1745 1719 26 EV
## 3 2013 5 13 2040 2025 15 2225 2226 -1 EV
## 4 2013 3 23 1914 1910 4 2045 2043 2 EV
## 5 2013 1 12 1559 1600 -1 1849 1917 -28 DL
## 6 2013 11 17 650 655 -5 1059 1150 -51 DL
## 7 2013 2 21 2355 2358 -3 412 438 -26 B6
## 8 2013 11 17 759 800 -1 1212 1255 -43 AA
## 9 2013 11 16 2003 1925 38 17 36 -19 DL
## 10 2013 11 16 2349 2359 -10 402 440 -38 B6
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay- Which flights travelled the farthest? Which travelled the shortest?
arrange(flights, desc(distance))## # A tibble: 336,776 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 857 900 -3 1516 1530 -14 HA
## 2 2013 1 2 909 900 9 1525 1530 -5 HA
## 3 2013 1 3 914 900 14 1504 1530 -26 HA
## 4 2013 1 4 900 900 0 1516 1530 -14 HA
## 5 2013 1 5 858 900 -2 1519 1530 -11 HA
## 6 2013 1 6 1019 900 79 1558 1530 28 HA
## 7 2013 1 7 1042 900 102 1620 1530 50 HA
## 8 2013 1 8 901 900 1 1504 1530 -26 HA
## 9 2013 1 9 641 900 1301 1242 1530 1272 HA
## 10 2013 1 10 859 900 -1 1449 1530 -41 HA
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delayarrange(flights, distance)## # A tibble: 336,776 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 7 27 NA 106 NA NA 245 NA US
## 2 2013 1 3 2127 2129 -2 2222 2224 -2 EV
## 3 2013 1 4 1240 1200 40 1333 1306 27 EV
## 4 2013 1 4 1829 1615 134 1937 1721 136 EV
## 5 2013 1 4 2128 2129 -1 2218 2224 -6 EV
## 6 2013 1 5 1155 1200 -5 1241 1306 -25 EV
## 7 2013 1 6 2125 2129 -4 2224 2224 0 EV
## 8 2013 1 7 2124 2129 -5 2212 2224 -12 EV
## 9 2013 1 8 2127 2130 -3 2304 2225 39 EV
## 10 2013 1 9 2126 2129 -3 2217 2224 -7 EV
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay5.4.1
- Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.
select(flights, dep_time, dep_delay, arr_time, arr_delay)## # A tibble: 336,776 x 4
## dep_time dep_delay arr_time arr_delay
## <int> <dbl> <int> <dbl>
## 1 517 2 830 11
## 2 533 4 850 20
## 3 542 2 923 33
## 4 544 -1 1004 -18
## 5 554 -6 812 -25
## 6 554 -4 740 12
## 7 555 -5 913 19
## 8 557 -3 709 -14
## 9 557 -3 838 -8
## 10 558 -2 753 8
## # … with 336,766 more rowsselect(flights, any_of(c("dep_time", "dep_delay", "arr_time", "arr_delay")))## # A tibble: 336,776 x 4
## dep_time dep_delay arr_time arr_delay
## <int> <dbl> <int> <dbl>
## 1 517 2 830 11
## 2 533 4 850 20
## 3 542 2 923 33
## 4 544 -1 1004 -18
## 5 554 -6 812 -25
## 6 554 -4 740 12
## 7 555 -5 913 19
## 8 557 -3 709 -14
## 9 557 -3 838 -8
## 10 558 -2 753 8
## # … with 336,766 more rowsselect(flights, starts_with("dep_"), starts_with("arr_"))## # A tibble: 336,776 x 4
## dep_time dep_delay arr_time arr_delay
## <int> <dbl> <int> <dbl>
## 1 517 2 830 11
## 2 533 4 850 20
## 3 542 2 923 33
## 4 544 -1 1004 -18
## 5 554 -6 812 -25
## 6 554 -4 740 12
## 7 555 -5 913 19
## 8 557 -3 709 -14
## 9 557 -3 838 -8
## 10 558 -2 753 8
## # … with 336,766 more rows- What happens if you include the name of a variable multiple times in a select() call? Nothing happens and the select() function ignores the duplicate
select(flights, dep_time, dep_time)## # A tibble: 336,776 x 1
## dep_time
## <int>
## 1 517
## 2 533
## 3 542
## 4 544
## 5 554
## 6 554
## 7 555
## 8 557
## 9 557
## 10 558
## # … with 336,766 more rows- What does the any_of() function do? Why might it be helpful in conjunction with this vector?
vars <- c("year", "month", "day", "dep_delay", "arr_delay")
select(flights, any_of(vars)) #select all the variables in the vector## # A tibble: 336,776 x 5
## year month day dep_delay arr_delay
## <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 2 11
## 2 2013 1 1 4 20
## 3 2013 1 1 2 33
## 4 2013 1 1 -1 -18
## 5 2013 1 1 -6 -25
## 6 2013 1 1 -4 12
## 7 2013 1 1 -5 19
## 8 2013 1 1 -3 -14
## 9 2013 1 1 -3 -8
## 10 2013 1 1 -2 8
## # … with 336,766 more rows- Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?
select(flights, contains("TIME"))## # A tibble: 336,776 x 6
## dep_time sched_dep_time arr_time sched_arr_time air_time time_hour
## <int> <int> <int> <int> <dbl> <dttm>
## 1 517 515 830 819 227 2013-01-01 05:00:00
## 2 533 529 850 830 227 2013-01-01 05:00:00
## 3 542 540 923 850 160 2013-01-01 05:00:00
## 4 544 545 1004 1022 183 2013-01-01 05:00:00
## 5 554 600 812 837 116 2013-01-01 06:00:00
## 6 554 558 740 728 150 2013-01-01 05:00:00
## 7 555 600 913 854 158 2013-01-01 06:00:00
## 8 557 600 709 723 53 2013-01-01 06:00:00
## 9 557 600 838 846 140 2013-01-01 06:00:00
## 10 558 600 753 745 138 2013-01-01 06:00:00
## # … with 336,766 more rows#This is not surprising to ignore uppercase character and makes it easier to search
select(flights, contains("TIME", ignore.case = FALSE))## # A tibble: 336,776 x 05.5.2
- Currently dep_time and sched_dep_time are convenient to look at, but hard to compute with because they’re not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight.
flights %>%
transmute(
dep_time,
hour1 = dep_time %/% 100,
minute1 = dep_time %% 100,
dep_time = hour1*60 + minute1,
sched_dep_time,
hour2 = sched_dep_time %/% 100,
minute2 = sched_dep_time %% 100,
sched_dep_time = hour2*60 + minute2)## # A tibble: 336,776 x 6
## dep_time hour1 minute1 sched_dep_time hour2 minute2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 317 5 17 315 5 15
## 2 333 5 33 329 5 29
## 3 342 5 42 340 5 40
## 4 344 5 44 345 5 45
## 5 354 5 54 360 6 0
## 6 354 5 54 358 5 58
## 7 355 5 55 360 6 0
## 8 357 5 57 360 6 0
## 9 357 5 57 360 6 0
## 10 358 5 58 360 6 0
## # … with 336,766 more rows- Compare air_time with arr_time - dep_time. What do you expect to see? What do you see? What do you need to do to fix it?
flights %>%
transmute(
dep_time,
hour1 = dep_time %/% 100,
minute1 = dep_time %% 100,
dep_time = hour1*60 + minute1,
arr_time,
hour2 = arr_time %/% 100,
minute2 = arr_time %% 100,
arr_time = hour2*60 + minute2,
air_time,
diff = arr_time - dep_time)## # A tibble: 336,776 x 8
## dep_time hour1 minute1 arr_time hour2 minute2 air_time diff
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 317 5 17 510 8 30 227 193
## 2 333 5 33 530 8 50 227 197
## 3 342 5 42 563 9 23 160 221
## 4 344 5 44 604 10 4 183 260
## 5 354 5 54 492 8 12 116 138
## 6 354 5 54 460 7 40 150 106
## 7 355 5 55 553 9 13 158 198
## 8 357 5 57 429 7 9 53 72
## 9 357 5 57 518 8 38 140 161
## 10 358 5 58 473 7 53 138 115
## # … with 336,766 more rowsair_time and arr_time - dep_time is different even though they should be the same This could be a change in time zones or entering a new day
- Compare dep_time, sched_dep_time, and dep_delay. How would you expect those three numbers to be related?
flights %>%
transmute(dep_delay,
dep_time = (dep_time %/% 100 * 60 + dep_time %% 100),
sched_dep_time = (sched_dep_time %/% 100 * 60 + sched_dep_time %% 100),
dep_delay_diff = dep_delay - dep_time + sched_dep_time)## # A tibble: 336,776 x 4
## dep_delay dep_time sched_dep_time dep_delay_diff
## <dbl> <dbl> <dbl> <dbl>
## 1 2 317 315 0
## 2 4 333 329 0
## 3 2 342 340 0
## 4 -1 344 345 0
## 5 -6 354 360 0
## 6 -4 354 358 0
## 7 -5 355 360 0
## 8 -3 357 360 0
## 9 -3 357 360 0
## 10 -2 358 360 0
## # … with 336,766 more rows#dep_delay should be equals to dep_time - schedule_dep_time;
# which is what we see with difference being 0- Find the 10 most delayed flights using a ranking function. How do you want to handle ties? Carefully read the documentation for min_rank().
arrange(flights, desc(dep_delay))## # A tibble: 336,776 x 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 9 641 900 1301 1242 1530 1272 HA
## 2 2013 6 15 1432 1935 1137 1607 2120 1127 MQ
## 3 2013 1 10 1121 1635 1126 1239 1810 1109 MQ
## 4 2013 9 20 1139 1845 1014 1457 2210 1007 AA
## 5 2013 7 22 845 1600 1005 1044 1815 989 MQ
## 6 2013 4 10 1100 1900 960 1342 2211 931 DL
## 7 2013 3 17 2321 810 911 135 1020 915 DL
## 8 2013 6 27 959 1900 899 1236 2226 850 DL
## 9 2013 7 22 2257 759 898 121 1026 895 DL
## 10 2013 12 5 756 1700 896 1058 2020 878 AA
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delayflights %>%
mutate(
rank = min_rank(desc(dep_delay))
)## # A tibble: 336,776 x 20
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 517 515 2 830 819 11 UA
## 2 2013 1 1 533 529 4 850 830 20 UA
## 3 2013 1 1 542 540 2 923 850 33 AA
## 4 2013 1 1 544 545 -1 1004 1022 -18 B6
## 5 2013 1 1 554 600 -6 812 837 -25 DL
## 6 2013 1 1 554 558 -4 740 728 12 UA
## 7 2013 1 1 555 600 -5 913 854 19 B6
## 8 2013 1 1 557 600 -3 709 723 -14 EV
## 9 2013 1 1 557 600 -3 838 846 -8 B6
## 10 2013 1 1 558 600 -2 753 745 8 AA
## # … with 336,766 more rows, 10 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, rank <int>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delayWhat does 1:3 + 1:10 return? Why? 1:3 + 1:10 It returns a vector that contains values that were the sum of the two vectors The equation goes: 1+1, 2+2, 3+3, 1+4, 2+5, 3+6, 1+7, 2+8, 3+9, 1+10 The length is equal to the length of 1:10 (warning message)
What trigonometric functions does R provide? We can find out by ?Trig There are cos(), sin(), tan(), acos(), asin(), atan(x), atan2(y, x), cospi(), sinpi(), tanpi()
5.6.7 Exercises
- Brainstorm at least 5 different ways to assess the typical delay characteristics of a group of flights. Consider the following scenarios: A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of the time A flight is always 10 mins late A fight is 30 minutes early 50% of the time, and 30 minutes late 50% of the time. 99% of the time a flight is on time. 1% of the time it’s 2 hours late. What is more important: arrival delay or departure delay?
If thinking from passengers’ perspectives, arrival delay is more important as it affects their next plans or even the next connecting flights. This is probability. A flight might be 99% on time, but that 1% long delay can affect people more than frequent 10 minutes.
- Come up with another approach that will give you the same output as not_cancelled %>% count(dest)
flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay)) %>%
group_by(dest) %>%
summarise(count = n())## # A tibble: 104 x 2
## dest count
## <chr> <int>
## 1 ABQ 254
## 2 ACK 264
## 3 ALB 418
## 4 ANC 8
## 5 ATL 16837
## 6 AUS 2411
## 7 AVL 261
## 8 BDL 412
## 9 BGR 358
## 10 BHM 269
## # … with 94 more rowsAnd alternative to not_cancelled %>% count(tailnum, wt = distance) (without using count()).
flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay)) %>%
group_by(tailnum) %>%
summarise(count = sum(distance))## # A tibble: 4,037 x 2
## tailnum count
## <chr> <dbl>
## 1 D942DN 3418
## 2 N0EGMQ 239143
## 3 N10156 109664
## 4 N102UW 25722
## 5 N103US 24619
## 6 N104UW 24616
## 7 N10575 139903
## 8 N105UW 23618
## 9 N107US 21677
## 10 N108UW 32070
## # … with 4,027 more rows- Our definition of cancelled flights (is.na(dep_delay) | is.na(arr_delay) ) is slightly suboptimal. Why? Which is the most important column?
The flights can be redirected or landed at another airport, which might not show up in arr_delay or dep_delay We can look at the actual airtime of the flight (air_time)
- Look at the number of cancelled flights per day.
flights %>%
mutate(cancel = is.na(arr_time)) %>%
group_by(year, month, day) %>%
summarise(count = n(),
cancelled = sum(cancel)
) %>%
ggplot() +
geom_point(aes(x = count, y = cancelled))## `summarise()` has grouped output by 'year', 'month'. You can override using the
## `.groups` argument.
#More delays seems to be correlated with more flights
#Is there a pattern? Is the proportion of cancelled flights related to the average delay?
flights %>%
mutate(cancel = is.na(arr_time)) %>%
group_by(year, month, day) %>%
summarise(delay = mean(arr_delay, na.rm = TRUE),
cancelled = sum(cancel)
) %>%
ggplot() +
geom_point(aes(x = delay, y = cancelled))## `summarise()` has grouped output by 'year', 'month'. You can override using the
## `.groups` argument.
#similarly, there are likely more cancelled flights with higher delay rates- Which carrier has the worst delays?
flights%>%
group_by(carrier) %>%
summarise(delay = mean(arr_delay, na.rm = TRUE)) %>%
arrange(desc(delay))## # A tibble: 16 x 2
## carrier delay
## <chr> <dbl>
## 1 F9 21.9
## 2 FL 20.1
## 3 EV 15.8
## 4 YV 15.6
## 5 OO 11.9
## 6 MQ 10.8
## 7 WN 9.65
## 8 B6 9.46
## 9 9E 7.38
## 10 UA 3.56
## 11 US 2.13
## 12 VX 1.76
## 13 DL 1.64
## 14 AA 0.364
## 15 HA -6.92
## 16 AS -9.93#F9 has the worst delayChallenge: can you disentangle the effects of bad airports vs. bad carriers? Why/why not? (Hint: think about flights %>% group_by(carrier, dest) %>% summarise(n()))
flights %>%
group_by(carrier, dest) %>%
summarise(delay = mean(arr_delay, na.rm = TRUE),
count = n()) %>%
arrange(desc(delay))## `summarise()` has grouped output by 'carrier'. You can override using the
## `.groups` argument.## # A tibble: 314 x 4
## # Groups: carrier [16]
## carrier dest delay count
## <chr> <chr> <dbl> <int>
## 1 UA STL 110 2
## 2 OO ORD 107 1
## 3 OO DTW 68.5 2
## 4 UA RDU 56 1
## 5 EV CAE 42.8 113
## 6 EV TYS 41.2 323
## 7 EV PBI 40.7 6
## 8 EV TUL 33.7 315
## 9 EV OKC 30.6 346
## 10 UA JAC 29.9 23
## # … with 304 more rows- What does the sort argument to count() do. When might you use it?
The count() argument sort data in the order of observations(n)
5.7.1 Exercises
- Refer back to the lists of useful mutate and filtering functions. Describe how each operation changes when you combine it with grouping.
The mutate and filtering functions would be apply to an entire variable/ column after grouping, they will be applied to the groups we set.
- Which plane (tailnum) has the worst on-time record?
flights %>%
group_by(tailnum) %>%
filter(!is.na(arr_delay)) %>%
summarise(delay = mean(arr_delay)) %>%
arrange(desc(delay))## # A tibble: 4,037 x 2
## tailnum delay
## <chr> <dbl>
## 1 N844MH 320
## 2 N911DA 294
## 3 N922EV 276
## 4 N587NW 264
## 5 N851NW 219
## 6 N928DN 201
## 7 N7715E 188
## 8 N654UA 185
## 9 N665MQ 175.
## 10 N427SW 157
## # … with 4,027 more rows#N844MH has the worst record- What time of day should you fly if you want to avoid delays as much as possible?
flights %>%
group_by(hour) %>%
filter(!is.na(dep_delay)) %>%
summarise(delay = mean(dep_delay))## # A tibble: 19 x 2
## hour delay
## <dbl> <dbl>
## 1 5 0.688
## 2 6 1.64
## 3 7 1.91
## 4 8 4.13
## 5 9 4.58
## 6 10 6.50
## 7 11 7.19
## 8 12 8.61
## 9 13 11.4
## 10 14 13.8
## 11 15 16.9
## 12 16 18.8
## 13 17 21.1
## 14 18 21.1
## 15 19 24.8
## 16 20 24.3
## 17 21 24.2
## 18 22 18.8
## 19 23 14.0#They should leave early in the morning (5-9 a.m.)- For each destination, compute the total minutes of delay.
flights %>%
group_by(dest) %>%
filter(!is.na(dep_delay)) %>%
summarise(delay = sum(dep_delay))## # A tibble: 104 x 2
## dest delay
## <chr> <dbl>
## 1 ABQ 3490
## 2 ACK 1711
## 3 ALB 9897
## 4 ANC 103
## 5 ATL 211391
## 6 AUS 31496
## 7 AVL 2154
## 8 BDL 7301
## 9 BGR 7011
## 10 BHM 8077
## # … with 94 more rows#For each flight, compute the proportion of the total delay for its destination.
flights %>%
group_by(dest) %>%
filter(!is.na(dep_delay)) %>%
summarise(delay = sum(dep_delay),
prop = dep_delay/ delay)## `summarise()` has grouped output by 'dest'. You can override using the `.groups`
## argument.## # A tibble: 328,521 x 3
## # Groups: dest [104]
## dest delay prop
## <chr> <dbl> <dbl>
## 1 ABQ 3490 -0.00172
## 2 ABQ 3490 0.00258
## 3 ABQ 3490 -0.00172
## 4 ABQ 3490 0.00458
## 5 ABQ 3490 0
## 6 ABQ 3490 -0.000573
## 7 ABQ 3490 0.000287
## 8 ABQ 3490 -0.00115
## 9 ABQ 3490 -0.00115
## 10 ABQ 3490 0.00287
## # … with 328,511 more rows- Delays are typically temporally correlated: even once the problem that caused the initial delay has been resolved, later flights are delayed to allow earlier flights to leave. Using lag(), explore how the delay of a flight is related to the delay of the immediately preceding flight.
flights %>%
group_by(origin) %>%
filter(!is.na(dep_delay)) %>%
mutate(lag = lag(dep_delay, default = 0)) %>%
ggplot() +
geom_point(aes(x = dep_delay, y = lag))
# the delay of the immediate preceding flight is positively correlated with the delay of the next flight- Look at each destination. Can you find flights that are suspiciously fast? (i.e. flights that represent a potential data entry error).
flights %>%
filter(!is.na(air_time)) %>%
select(dest, air_time, carrier, flight, tailnum, distance) %>%
group_by(dest) %>%
mutate(speed = distance/ air_time) %>%
arrange(speed)## # A tibble: 327,346 x 7
## # Groups: dest [104]
## dest air_time carrier flight tailnum distance speed
## <chr> <dbl> <chr> <int> <chr> <dbl> <dbl>
## 1 PHL 75 US 1860 N755US 96 1.28
## 2 ACK 141 B6 1491 N328JB 199 1.41
## 3 PHL 61 9E 3608 N8932C 94 1.54
## 4 PHL 59 9E 3667 N832AY 94 1.59
## 5 PHL 60 US 1909 N951UW 96 1.6
## 6 PHL 60 US 1289 N956UW 96 1.6
## 7 PHL 59 US 1775 N945UW 96 1.63
## 8 DCA 131 US 2175 N745VJ 214 1.63
## 9 PHL 57 US 1279 N953UW 96 1.68
## 10 PHL 55 9E 3638 N8631E 94 1.71
## # … with 327,336 more rowsCompute the air time of a flight relative to the shortest flight to that destination. Which flights were most delayed in the air?
flights %>%
filter(!is.na(air_time)) %>%
select(dest, air_time, carrier, flight, tailnum, distance) %>%
group_by(dest) %>%
mutate(speed = distance/ air_time) %>%
arrange(desc(speed))## # A tibble: 327,346 x 7
## # Groups: dest [104]
## dest air_time carrier flight tailnum distance speed
## <chr> <dbl> <chr> <int> <chr> <dbl> <dbl>
## 1 ATL 65 DL 1499 N666DN 762 11.7
## 2 MSP 93 EV 4667 N17196 1008 10.8
## 3 GSP 55 EV 4292 N14568 594 10.8
## 4 BNA 70 EV 3805 N12567 748 10.7
## 5 PBI 105 DL 1902 N956DL 1035 9.86
## 6 SJU 170 DL 315 N3768 1598 9.4
## 7 SJU 172 B6 707 N779JB 1598 9.29
## 8 STT 175 AA 936 N5FFAA 1623 9.27
## 9 SJU 173 DL 347 N3773D 1598 9.24
## 10 SJU 173 B6 1503 N571JB 1598 9.24
## # … with 327,336 more rows- Find all destinations that are flown by at least two carriers.
flights %>%
filter(!is.na(air_time)) %>%
group_by(dest) %>%
summarise(count = n_distinct(carrier, na.rm = TRUE)) %>%
filter(count > 1)## # A tibble: 75 x 2
## dest count
## <chr> <int>
## 1 ATL 7
## 2 AUS 6
## 3 AVL 2
## 4 BDL 2
## 5 BNA 5
## 6 BOS 7
## 7 BQN 2
## 8 BTV 3
## 9 BUF 4
## 10 BWI 4
## # … with 65 more rows#Use that information to rank the carriers.
flights %>%
filter(!is.na(air_time)) %>%
group_by(carrier) %>%
summarise(count = n_distinct(dest, na.rm = TRUE)) %>%
filter(count > 1) %>%
arrange(desc(count))## # A tibble: 13 x 2
## carrier count
## <chr> <int>
## 1 EV 61
## 2 9E 48
## 3 UA 47
## 4 B6 42
## 5 DL 40
## 6 MQ 20
## 7 AA 19
## 8 WN 11
## 9 OO 5
## 10 US 5
## 11 VX 5
## 12 FL 3
## 13 YV 3- For each plane, count the number of flights before the first delay of greater than 1 hour.
flights %>%
filter(!is.na(air_time)) %>%
select(tailnum, dep_delay, year, month, day) %>%
arrange(year, month, day) %>%
group_by(tailnum) %>%
mutate(delay = cumsum(dep_delay >60)) %>%
summarise(count = sum(delay))## # A tibble: 4,037 x 2
## tailnum count
## <chr> <int>
## 1 D942DN 4
## 2 N0EGMQ 4358
## 3 N10156 1668
## 4 N102UW 32
## 5 N103US 0
## 6 N104UW 106
## 7 N10575 6524
## 8 N105UW 23
## 9 N107US 21
## 10 N108UW 26
## # … with 4,027 more rowsChapter 6
6.3 Exercises
- Go to the RStudio Tips twitter account, https://twitter.com/rstudiotips and find one tip that looks interesting. Practice using it!
You can access R Cheatsheets straight from RStudio, by doing the following: Help in Menu Bar > Cheat Sheets > Browse Cheat Sheets…
Bonus tip: You select a number in RStudio, you can hit Option+Shift ↑ or Option+Shift ↓ to increment/decrement the number. We tried it in the Console. Cool!
- What other common mistakes will RStudio diagnostics report? Read https://support.rstudio.com/hc/en-us/articles/205753617-Code-Diagnostics to find out.
Some common mistakes the diagnostics report will reveal are: - Missing arguments, unmatched arguments, partially matched arguments, and too many arguments - If variable used has no definition in scope or if variable is defined but not used - Inappropriate use (or lack thereof) of whitespace
Bulut & Dejardins (2019)
Chapter 4
library(data.table)## Warning: package 'data.table' was built under R version 4.0.2##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transposedata.table(DT) has a general form DT[i, j, by] where i = on which row; j = what to do; and by = grouped by what
4.2.1 Exercises
Import Data Set The pisa data set is too large so I will use the region6 data instead
pisa <- fread("~/Desktop/Academics/2022 Fall/Applied Data Science/Data Sets/region6.csv", na.strings = "")
class(pisa)## [1] "data.table" "data.frame"print(object.size(pisa), unit = "GB")## 1.1 Gb4.3.1 Exercises
- Subset all the Female students (ST004D01T) in Germany
pisa[CNTRYID == "Germany" & ST004D01T == "Female"]## CNTRYID CNT CNTSCHID CNTSTUID CYC NatCen Region
## 1: Germany Germany 27600001 27605365 06MS Germany Germany
## 2: Germany Germany 27600001 27601858 06MS Germany Germany
## 3: Germany Germany 27600001 27602008 06MS Germany Germany
## 4: Germany Germany 27600001 27606950 06MS Germany Germany
## 5: Germany Germany 27600001 27611010 06MS Germany Germany
## ---
## 3193: Germany Germany 27600262 27601530 06MS Germany Germany
## 3194: Germany Germany 27600262 27606088 06MS Germany Germany
## 3195: Germany Germany 27600262 27606696 06MS Germany Germany
## 3196: Germany Germany 27600262 27606135 06MS Germany Germany
## 3197: Germany Germany 27600262 27611733 06MS Germany Germany
## STRATUM SUBNATIO OECD ADMINMODE Option_CPS
## 1: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 2: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 3: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 4: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 5: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## ---
## 3193: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 3194: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 3195: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 3196: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## 3197: Undisclosed STRATUM - Germany Germany Yes Computer Yes
## Option_FL Option_ICTQ Option_ECQ Option_PQ Option_TQ Option_UH
## 1: No Yes Yes Yes Yes Yes
## 2: No Yes Yes Yes Yes Yes
## 3: No Yes Yes Yes Yes Yes
## 4: No Yes Yes Yes Yes Yes
## 5: No Yes Yes Yes Yes Yes
## ---
## 3193: No Yes Yes Yes Yes Yes
## 3194: No Yes Yes Yes Yes Yes
## 3195: No Yes Yes Yes Yes Yes
## 3196: No Yes Yes Yes Yes Yes
## 3197: No Yes Yes Yes Yes Yes
## Option_Read Option_Math LANGTEST_QQQ LANGTEST_COG LANGTEST_PAQ
## 1: Standard Cluster Standard Cluster German German <NA>
## 2: Standard Cluster Standard Cluster German German German
## 3: Standard Cluster Standard Cluster German German German
## 4: Standard Cluster Standard Cluster German German <NA>
## 5: Standard Cluster Standard Cluster German German German
## ---
## 3193: Standard Cluster Standard Cluster German German German
## 3194: Standard Cluster Standard Cluster German German German
## 3195: Standard Cluster Standard Cluster German German German
## 3196: Standard Cluster Standard Cluster German German German
## 3197: Standard Cluster Standard Cluster German German <NA>
## CBASCI BOOKID ST001D01T ST003D02T ST003D03T
## 1: Science Random Number 1 Form 32 (CBA) Grade 9 January 1999
## 2: Science Random Number 3 Form 44 (CBA) Grade 9 October 1999
## 3: Science Random Number 4 Form 95 (CBA) Grade 9 November 1999
## 4: Science Random Number 4 Form 96 (CBA) Grade 10 June 1999
## 5: Science Random Number 5 Form 46 (CBA) Grade 10 March 1999
## ---
## 3193: Science Random Number 1 Form 43 (CBA) Grade 10 March 1999
## 3194: Science Random Number 2 Form 32 (CBA) Grade 10 May 1999
## 3195: Science Random Number 4 Form 44 (CBA) Grade 10 June 1999
## 3196: Science Random Number 5 Form 93 (CBA) Grade 10 March 1999
## 3197: Science Random Number 1 Form 90 (CBA) Grade 10 February 1999
## ST004D01T ST005Q01TA ST006Q01TA ST006Q02TA ST006Q03TA ST006Q04TA
## 1: Female <NA> <NA> <NA> <NA> <NA>
## 2: Female <ISCED level 2> No No No Yes
## 3: Female <ISCED level 2> No Yes No No
## 4: Female <ISCED level 3A> No Yes No No
## 5: Female <ISCED level 2> <NA> <NA> <NA> Yes
## ---
## 3193: Female <ISCED level 2> No No No No
## 3194: Female <ISCED level 2> No No No No
## 3195: Female <ISCED level 2> No No No No
## 3196: Female <ISCED level 2> No No Yes No
## 3197: Female <ISCED level 3A> <NA> <NA> <NA> Yes
## ST007Q01TA ST008Q01TA ST008Q02TA ST008Q03TA ST008Q04TA
## 1: <ISCED level 3A> <NA> <NA> Yes <NA>
## 2: <ISCED level 2> No No No No
## 3: <ISCED level 2> No No No No
## 4: <ISCED level 3A> Yes Yes No No
## 5: <ISCED level 2> <NA> <NA> <NA> <NA>
## ---
## 3193: <ISCED level 2> No No No No
## 3194: <ISCED level 3B, 3C> No No No No
## 3195: <ISCED level 2> No No No <NA>
## 3196: <ISCED level 3A> No No Yes No
## 3197: <ISCED level 2> <NA> Yes <NA> <NA>
## ST011Q01TA ST011Q02TA ST011Q03TA ST011Q04TA ST011Q05TA ST011Q06TA
## 1: Yes Yes Yes Yes Yes Yes
## 2: Yes Yes Yes Yes Yes Yes
## 3: Yes Yes Yes Yes No Yes
## 4: Yes Yes Yes Yes Yes Yes
## 5: Yes Yes Yes Yes Yes Yes
## ---
## 3193: Yes Yes Yes Yes Yes Yes
## 3194: Yes Yes Yes Yes No Yes
## 3195: No Yes Yes No No Yes
## 3196: Yes Yes Yes Yes Yes Yes
## 3197: Yes Yes Yes Yes No Yes
## ST011Q07TA ST011Q08TA ST011Q09TA ST011Q10TA ST011Q11TA ST011Q12TA
## 1: Yes Yes Yes Yes Yes Yes
## 2: No No No Yes Yes Yes
## 3: No No No Yes No Yes
## 4: Yes Yes Yes Yes Yes Yes
## 5: No Yes No Yes Yes Yes
## ---
## 3193: Yes Yes Yes Yes Yes Yes
## 3194: No No Yes Yes Yes Yes
## 3195: No No No Yes No Yes
## 3196: Yes Yes Yes Yes Yes Yes
## 3197: No Yes Yes Yes Yes Yes
## ST011Q16NA
## 1: Yes
## 2: Yes
## 3: No
## 4: Yes
## 5: Yes
## ---
## 3193: No
## 3194: Yes
## 3195: No
## 3196: No
## 3197: Yes
## ST011D17TA
## 1: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## 2: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## 3: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## 4: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - No
## 5: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## ---
## 3193: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## 3194: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## 3195: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## 3196: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## 3197: Germany : A game console (z. B. Playstation®, Nintendo®, X-Box®, Wii®) - Yes
## ST011D18TA ST011D19TA
## 1: Germany : A TV in your room - Yes Germany : Audiobooks - Yes
## 2: Germany : A TV in your room - Yes Germany : Audiobooks - No
## 3: Germany : A TV in your room - Yes Germany : Audiobooks - Yes
## 4: Germany : A TV in your room - Yes Germany : Audiobooks - No
## 5: Germany : A TV in your room - No Germany : Audiobooks - Yes
## ---
## 3193: Germany : A TV in your room - Yes Germany : Audiobooks - Yes
## 3194: Germany : A TV in your room - Yes Germany : Audiobooks - Yes
## 3195: Germany : A TV in your room - Yes Germany : Audiobooks - No
## 3196: Germany : A TV in your room - Yes Germany : Audiobooks - Yes
## 3197: Germany : A TV in your room - Yes Germany : Audiobooks - Yes
## ST012Q01TA ST012Q02TA ST012Q03TA ST012Q05NA ST012Q06NA
## 1: Three or more Two Two Three or more Three or more
## 2: Three or more Two One Three or more Two
## 3: Three or more Two Three or more Three or more Three or more
## 4: Three or more Two Two Three or more Three or more
## 5: Two One Two Three or more One
## ---
## 3193: Two One Two Three or more Two
## 3194: Three or more Three or more One Three or more Three or more
## 3195: Three or more One Two Three or more Three or more
## 3196: Three or more Two One Three or more Three or more
## 3197: Three or more Three or more Two Three or more Three or more
## ST012Q07NA ST012Q08NA ST012Q09NA ST013Q01TA ST123Q01NA
## 1: Two None Three or more 201-500 books Disagree
## 2: None None None 11-25 books Strongly agree
## 3: Two None Three or more 101-200 books Strongly agree
## 4: One One Two 201-500 books Agree
## 5: None None One 26-100 books Agree
## ---
## 3193: None None Two 101-200 books Strongly agree
## 3194: Two One One 26-100 books Strongly agree
## 3195: None One None 101-200 books Strongly agree
## 3196: One None One 201-500 books Strongly agree
## 3197: None None Three or more More than 500 books Strongly agree
## ST123Q02NA ST123Q03NA ST123Q04NA ST019AQ01T
## 1: Agree Agree Disagree Country of test
## 2: Strongly agree Strongly agree Strongly agree Country of test
## 3: Strongly agree Strongly agree Strongly agree Country of test
## 4: Strongly agree Agree Agree Country of test
## 5: Agree Strongly agree Strongly agree Country of test
## ---
## 3193: Strongly agree Strongly agree Strongly agree Country of test
## 3194: Strongly agree Strongly agree Strongly agree Country of test
## 3195: Disagree Strongly disagree Strongly disagree Country of test
## 3196: Strongly agree Strongly agree Strongly agree Country of test
## 3197: Strongly agree Strongly agree Strongly agree Country of test
## ST019BQ01T ST019CQ01T ST021Q01TA ST022Q01TA ST124Q01TA
## 1: Other country Country of test <NA> Language of test <NA>
## 2: Country of test Country of test <NA> Language of test <NA>
## 3: Country of test Country of test <NA> Language of test <NA>
## 4: Country of test Country of test <NA> Language of test <NA>
## 5: Country of test Country of test <NA> Language of test <NA>
## ---
## 3193: Country of test Country of test <NA> Language of test <NA>
## 3194: Country of test Other country <NA> Language of test <NA>
## 3195: Country of test Country of test <NA> Language of test <NA>
## 3196: Country of test Other country <NA> Language of test <NA>
## 3197: Other country Country of test <NA> Language of test <NA>
## ST125Q01NA ST126Q01TA ST127Q01TA ST127Q02TA ST127Q03TA ST111Q01TA
## 1: 3 years 6 <NA> Yes, once <NA> <ISCED level 3A>
## 2: 3 years 6 No, never No, never <NA> <ISCED level 3A>
## 3: 3 years 6 No, never No, never <NA> <ISCED level 3A>
## 4: 3 years 6 No, never No, never <NA> <ISCED level 3A>
## 5: 3 years 6 No, never No, never <NA> <ISCED level 3A>
## ---
## 3193: 4 years 6 No, never No, never <NA> <ISCED level 3A>
## 3194: 3 years 6 No, never No, never <NA> <ISCED level 2>
## 3195: 3 years 7 No, never No, never <NA> <ISCED level 3A>
## 3196: 3 years 6 No, never No, never <NA> <ISCED level 2>
## 3197: 4 years 6 No, never No, never <NA> <ISCED level 2>
## ST118Q01NA ST118Q02NA ST118Q03NA ST118Q04NA
## 1: Agree Agree Disagree Disagree
## 2: Agree Agree Disagree Strongly disagree
## 3: Disagree Disagree Agree Disagree
## 4: Agree Disagree Agree Disagree
## 5: Disagree Disagree Disagree Agree
## ---
## 3193: Agree Agree Agree Disagree
## 3194: Agree Agree Disagree Disagree
## 3195: Strongly agree Strongly agree Disagree Disagree
## 3196: Agree Agree Strongly agree Strongly agree
## 3197: Strongly agree Strongly agree Agree Disagree
## ST118Q05NA ST119Q01NA ST119Q02NA ST119Q03NA
## 1: Agree Strongly disagree Disagree Strongly disagree
## 2: Strongly disagree Disagree Agree Disagree
## 3: Disagree Strongly agree Strongly agree Agree
## 4: Strongly disagree Agree Strongly agree Disagree
## 5: Agree Agree Agree Disagree
## ---
## 3193: Agree Strongly agree Strongly agree Agree
## 3194: Disagree Agree Agree Disagree
## 3195: Agree Agree Agree Strongly disagree
## 3196: Strongly agree Disagree Disagree Disagree
## 3197: Agree Agree Disagree Agree
## ST119Q04NA ST119Q05NA ST121Q01NA ST121Q02NA
## 1: Strongly disagree Strongly disagree Strongly disagree Agree
## 2: Agree Disagree Strongly disagree Agree
## 3: Agree Agree Strongly disagree Agree
## 4: Agree Agree Strongly disagree Strongly agree
## 5: Agree Agree Strongly disagree Strongly agree
## ---
## 3193: Disagree Strongly agree Strongly disagree Strongly agree
## 3194: Disagree Disagree Strongly disagree Strongly agree
## 3195: Disagree Strongly disagree Strongly disagree Agree
## 3196: Disagree Strongly disagree Strongly disagree Agree
## 3197: Strongly agree Agree Disagree Agree
## ST121Q03NA ST082Q01NA ST082Q02NA ST082Q03NA
## 1: Strongly agree Agree Agree Agree
## 2: Strongly agree Agree Strongly agree Agree
## 3: Strongly agree Agree Strongly agree Strongly agree
## 4: Strongly agree Strongly agree Agree Agree
## 5: Strongly agree Agree Strongly agree Agree
## ---
## 3193: Strongly agree Agree Strongly agree Agree
## 3194: Strongly agree Strongly agree Strongly agree Strongly agree
## 3195: Strongly agree Disagree Agree Disagree
## 3196: Strongly agree Strongly disagree Agree Agree
## 3197: Strongly agree Agree Strongly agree Agree
## ST082Q08NA ST082Q09NA ST082Q12NA ST082Q13NA
## 1: Agree Disagree Agree Agree
## 2: Strongly agree Strongly agree Agree Agree
## 3: Strongly agree Agree Strongly agree Disagree
## 4: Agree Disagree Agree Disagree
## 5: Agree Disagree Disagree Disagree
## ---
## 3193: Agree Agree Agree Disagree
## 3194: Strongly agree Strongly agree Strongly agree Strongly agree
## 3195: Agree Strongly disagree Disagree Disagree
## 3196: Disagree Disagree Strongly agree Strongly disagree
## 3197: Strongly agree Disagree Agree Disagree
## ST082Q14NA ST034Q01TA ST034Q02TA ST034Q03TA
## 1: Strongly agree Strongly disagree Disagree Agree
## 2: Agree Strongly disagree Agree Agree
## 3: Strongly agree Strongly disagree Strongly agree Strongly agree
## 4: Strongly agree Strongly disagree Strongly agree Strongly agree
## 5: Agree Disagree Agree Disagree
## ---
## 3193: Agree Strongly disagree Agree Agree
## 3194: Strongly agree Strongly disagree Strongly agree Strongly agree
## 3195: Agree Strongly disagree Agree Disagree
## 3196: Agree Disagree Agree Agree
## 3197: Agree Agree Disagree Strongly disagree
## ST034Q04TA ST034Q05TA ST034Q06TA ST039Q01NA
## 1: Strongly disagree Agree Disagree A few times a year
## 2: Strongly disagree Agree Strongly disagree A few times a month
## 3: Strongly disagree Agree Strongly disagree Never or almost never
## 4: Strongly disagree Agree Strongly disagree A few times a month
## 5: Agree Disagree Agree Once a week or more
## ---
## 3193: Strongly disagree Agree Strongly disagree A few times a month
## 3194: Strongly disagree Strongly agree Strongly disagree A few times a year
## 3195: Strongly agree Agree Disagree Never or almost never
## 3196: Strongly disagree Agree Strongly disagree A few times a year
## 3197: Strongly agree Agree Disagree Once a week or more
## ST039Q02NA ST039Q03NA ST039Q04NA
## 1: A few times a year A few times a year Never or almost never
## 2: Never or almost never Never or almost never Never or almost never
## 3: Never or almost never Never or almost never Never or almost never
## 4: Never or almost never Never or almost never Never or almost never
## 5: A few times a year A few times a year Never or almost never
## ---
## 3193: Never or almost never Never or almost never Never or almost never
## 3194: Never or almost never Never or almost never Never or almost never
## 3195: A few times a month Once a week or more Once a week or more
## 3196: A few times a month Once a week or more A few times a year
## 3197: A few times a month Once a week or more A few times a year
## ST039Q05NA ST039Q06NA ST059Q01TA ST059Q02TA
## 1: Never or almost never Never or almost never 4 4
## 2: Never or almost never Never or almost never 4 4
## 3: Never or almost never Never or almost never 4 4
## 4: Never or almost never Never or almost never 4 4
## 5: A few times a year Never or almost never 4 4
## ---
## 3193: Never or almost never Never or almost never 4 4
## 3194: Never or almost never Never or almost never 4 4
## 3195: Never or almost never Never or almost never 4 4
## 3196: Never or almost never A few times a year 4 4
## 3197: A few times a year Never or almost never 4 4
## ST059Q03TA ST060Q01NA ST061Q01NA ST062Q01TA ST062Q02TA
## 1: 9 30 45 None None
## 2: 6 36 45 None None
## 3: 6 36 90 None None
## 4: 0 33 45 None None
## 5: 6 36 45 None None
## ---
## 3193: 6 30 90 None None
## 3194: 6 32 45 None None
## 3195: 6 32 45 None One or two times
## 3196: 2 60 45 None None
## 3197: 4 45 45 None None
## ST062Q03TA ST071Q01NA ST071Q02NA ST071Q03NA ST071Q04NA ST071Q05NA
## 1: None 1 2 NA 2 1
## 2: None 4 6 1 1 NA
## 3: None 1 1 1 5 1
## 4: None NA 1 1 2 1
## 5: None 0 0 0 4 1
## ---
## 3193: None 3 2 2 5 NA
## 3194: None 6 4 2 1 3
## 3195: None 0 1 0 0 1
## 3196: One or two times NA 4 2 NA 1
## 3197: None NA 8 3 4 3
## ST031Q01NA ST032Q01NA ST032Q02NA ST063Q01NA ST063Q01NB ST063Q02NA
## 1: 1 day 3 days 1 day Checked Not checked Checked
## 2: 1 day 2 days 2 days Checked Checked Checked
## 3: 1 day 7 days 4 days Checked Checked Checked
## 4: 2 days 2 days 5 days Checked Not checked Checked
## 5: 2 days 7 days 4 days Checked Not checked Checked
## ---
## 3193: 3 days 6 days 5 days Checked Checked Checked
## 3194: 1 day 7 days 4 days Checked Not checked Checked
## 3195: 1 day 4 days 3 days Checked Not checked Checked
## 3196: 1 day 7 days 3 days Checked Not checked Checked
## 3197: 1 day 4 days 4 days Checked Checked Checked
## ST063Q02NB ST063Q03NA ST063Q03NB ST063Q04NA ST063Q04NB ST063Q05NA
## 1: Not checked Checked Not checked Checked Not checked Not checked
## 2: Checked Checked Checked Not checked Not checked Not checked
## 3: Checked Checked Checked Checked Checked Not checked
## 4: Not checked Checked Not checked Not checked Not checked Not checked
## 5: Not checked Checked Not checked Not checked Not checked Not checked
## ---
## 3193: Checked Checked Checked Not checked Not checked Not checked
## 3194: Not checked Checked Not checked Not checked Not checked Not checked
## 3195: Not checked Checked Not checked Not checked Not checked Not checked
## 3196: Not checked Checked Not checked Not checked Not checked Not checked
## 3197: Checked Checked Checked Not checked Not checked Not checked
## ST063Q05NB ST063Q06NA ST063Q06NB ST064Q01NA
## 1: Not checked Not checked Not checked Yes, to a certain degree
## 2: Not checked Not checked Not checked Yes, to a certain degree
## 3: Not checked Not checked Not checked Yes, to a certain degree
## 4: Not checked Not checked Not checked Yes, to a certain degree
## 5: Not checked Not checked Not checked Yes, to a certain degree
## ---
## 3193: Not checked Not checked Not checked No, not at all
## 3194: Checked Not checked Checked No, not at all
## 3195: Not checked Not checked Not checked No, not at all
## 3196: Not checked Not checked Not checked Yes, to a certain degree
## 3197: Not checked Not checked Not checked No, not at all
## ST064Q02NA ST064Q03NA ST097Q01TA ST097Q02TA
## 1: No, not at all No, not at all Some lessons Some lessons
## 2: No, not at all No, not at all Some lessons Some lessons
## 3: No, not at all No, not at all Some lessons Never or hardly ever
## 4: No, not at all Yes, to a certain degree Some lessons Some lessons
## 5: No, not at all No, not at all Most lessons Never or hardly ever
## ---
## 3193: No, not at all No, not at all Some lessons Never or hardly ever
## 3194: No, not at all No, not at all Some lessons Never or hardly ever
## 3195: No, not at all No, not at all Some lessons Never or hardly ever
## 3196: No, not at all No, not at all Most lessons Some lessons
## 3197: No, not at all No, not at all Some lessons Some lessons
## ST097Q03TA ST097Q04TA ST097Q05TA
## 1: Some lessons Some lessons Most lessons
## 2: Some lessons Never or hardly ever Some lessons
## 3: Some lessons Never or hardly ever Some lessons
## 4: Some lessons Some lessons Some lessons
## 5: Never or hardly ever Some lessons Never or hardly ever
## ---
## 3193: Never or hardly ever Never or hardly ever Never or hardly ever
## 3194: Some lessons Never or hardly ever Never or hardly ever
## 3195: <NA> Never or hardly ever Some lessons
## 3196: Some lessons Some lessons Some lessons
## 3197: Some lessons Some lessons Some lessons
## ST098Q01TA ST098Q02TA ST098Q03NA ST098Q05TA
## 1: In most lessons In some lessons Never or hardly ever <NA>
## 2: In most lessons In some lessons In some lessons In some lessons
## 3: In all lessons In most lessons In most lessons In all lessons
## 4: In most lessons In some lessons In some lessons In most lessons
## 5: In most lessons Never or hardly ever In most lessons In some lessons
## ---
## 3193: In most lessons In some lessons In some lessons In most lessons
## 3194: In all lessons In some lessons In all lessons In most lessons
## 3195: In all lessons In some lessons Never or hardly ever In some lessons
## 3196: In all lessons In some lessons In some lessons In all lessons
## 3197: In all lessons In most lessons In all lessons In most lessons
## ST098Q06TA ST098Q07TA ST098Q08NA
## 1: In most lessons Never or hardly ever Never or hardly ever
## 2: In some lessons In some lessons In most lessons
## 3: In all lessons In some lessons In some lessons
## 4: In most lessons Never or hardly ever In some lessons
## 5: In most lessons In some lessons In most lessons
## ---
## 3193: In some lessons Never or hardly ever In some lessons
## 3194: In most lessons In most lessons In most lessons
## 3195: Never or hardly ever Never or hardly ever Never or hardly ever
## 3196: In most lessons Never or hardly ever In some lessons
## 3197: In all lessons In most lessons In most lessons
## ST098Q09TA ST098Q10NA ST100Q01TA
## 1: Never or hardly ever In some lessons Some lessons
## 2: Never or hardly ever Never or hardly ever Every lesson
## 3: In most lessons In most lessons Every lesson
## 4: In most lessons In some lessons Every lesson
## 5: In most lessons In most lessons Some lessons
## ---
## 3193: In most lessons Never or hardly ever Every lesson
## 3194: In most lessons In most lessons Most lessons
## 3195: Never or hardly ever Never or hardly ever Some lessons
## 3196: Never or hardly ever In most lessons Never or hardly ever
## 3197: In all lessons In some lessons Most lessons
## ST100Q02TA ST100Q03TA ST100Q04TA
## 1: Most lessons Some lessons Most lessons
## 2: Every lesson Most lessons Every lesson
## 3: Every lesson Every lesson Every lesson
## 4: Every lesson Every lesson Most lessons
## 5: Most lessons Most lessons Some lessons
## ---
## 3193: Every lesson Most lessons Every lesson
## 3194: Every lesson Every lesson Every lesson
## 3195: Most lessons Most lessons Most lessons
## 3196: Most lessons Some lessons Never or hardly ever
## 3197: Some lessons Never or hardly ever Some lessons
## ST100Q05TA ST103Q01NA
## 1: Never or hardly ever Some lessons
## 2: Most lessons Many lessons
## 3: Every lesson Every lesson or almost every lesson
## 4: Most lessons Many lessons
## 5: Most lessons Many lessons
## ---
## 3193: Every lesson Many lessons
## 3194: Every lesson Every lesson or almost every lesson
## 3195: Most lessons Never or almost never
## 3196: Most lessons Some lessons
## 3197: Every lesson Some lessons
## ST103Q03NA ST103Q08NA
## 1: Never or almost never Some lessons
## 2: Some lessons Some lessons
## 3: Never or almost never Many lessons
## 4: Many lessons Many lessons
## 5: Some lessons Many lessons
## ---
## 3193: Many lessons Many lessons
## 3194: Every lesson or almost every lesson Every lesson or almost every lesson
## 3195: Never or almost never Some lessons
## 3196: Many lessons Many lessons
## 3197: Every lesson or almost every lesson Every lesson or almost every lesson
## ST103Q11NA ST104Q01NA
## 1: Some lessons Never or almost never
## 2: Never or almost never Some lessons
## 3: Every lesson or almost every lesson Every lesson or almost every lesson
## 4: Never or almost never Some lessons
## 5: Some lessons Some lessons
## ---
## 3193: Some lessons Some lessons
## 3194: Every lesson or almost every lesson Some lessons
## 3195: Never or almost never Some lessons
## 3196: Some lessons Some lessons
## 3197: Some lessons Some lessons
## ST104Q02NA ST104Q03NA ST104Q04NA
## 1: Never or almost never Never or almost never Never or almost never
## 2: Never or almost never Never or almost never <NA>
## 3: Never or almost never Some lessons Some lessons
## 4: Some lessons Some lessons Some lessons
## 5: Never or almost never Never or almost never Never or almost never
## ---
## 3193: Never or almost never Never or almost never Some lessons
## 3194: Some lessons Some lessons Some lessons
## 3195: Never or almost never Some lessons Some lessons
## 3196: Never or almost never Some lessons Never or almost never
## 3197: Some lessons Some lessons Some lessons
## ST104Q05NA ST107Q01NA
## 1: Never or almost never Never or almost never
## 2: Never or almost never Many lessons
## 3: Some lessons Some lessons
## 4: Some lessons Many lessons
## 5: Never or almost never Some lessons
## ---
## 3193: Some lessons Many lessons
## 3194: Some lessons Many lessons
## 3195: Never or almost never Never or almost never
## 3196: Never or almost never Never or almost never
## 3197: Some lessons Every lesson or almost every lesson
## ST107Q02NA ST107Q03NA
## 1: Every lesson or almost every lesson Some lessons
## 2: Never or almost never Never or almost never
## 3: Every lesson or almost every lesson Never or almost never
## 4: Many lessons Some lessons
## 5: Never or almost never Many lessons
## ---
## 3193: Many lessons Many lessons
## 3194: Many lessons Some lessons
## 3195: Some lessons Never or almost never
## 3196: Never or almost never Never or almost never
## 3197: Many lessons Many lessons
## ST092Q01TA
## 1: I have never heard of this
## 2: I have never heard of this
## 3: I know something about this and could explain the general issue
## 4: I have heard about this but I would not be able to explain what it is really about
## 5: I know something about this and could explain the general issue
## ---
## 3193: I am familiar with this and I would be able to explain this well
## 3194: I know something about this and could explain the general issue
## 3195: I know something about this and could explain the general issue
## 3196: I am familiar with this and I would be able to explain this well
## 3197: I am familiar with this and I would be able to explain this well
## ST092Q02TA
## 1: I have heard about this but I would not be able to explain what it is really about
## 2: I have never heard of this
## 3: I have never heard of this
## 4: I have heard about this but I would not be able to explain what it is really about
## 5: I have never heard of this
## ---
## 3193: I have never heard of this
## 3194: I have heard about this but I would not be able to explain what it is really about
## 3195: I have heard about this but I would not be able to explain what it is really about
## 3196: I know something about this and could explain the general issue
## 3197: I know something about this and could explain the general issue
## ST092Q04TA
## 1: I have heard about this but I would not be able to explain what it is really about
## 2: I have heard about this but I would not be able to explain what it is really about
## 3: I have heard about this but I would not be able to explain what it is really about
## 4: I know something about this and could explain the general issue
## 5: I know something about this and could explain the general issue
## ---
## 3193: I am familiar with this and I would be able to explain this well
## 3194: I know something about this and could explain the general issue
## 3195: I am familiar with this and I would be able to explain this well
## 3196: I am familiar with this and I would be able to explain this well
## 3197: I am familiar with this and I would be able to explain this well
## ST092Q05TA
## 1: I know something about this and could explain the general issue
## 2: I have heard about this but I would not be able to explain what it is really about
## 3: I am familiar with this and I would be able to explain this well
## 4: I know something about this and could explain the general issue
## 5: I am familiar with this and I would be able to explain this well
## ---
## 3193: I am familiar with this and I would be able to explain this well
## 3194: I have heard about this but I would not be able to explain what it is really about
## 3195: I am familiar with this and I would be able to explain this well
## 3196: I am familiar with this and I would be able to explain this well
## 3197: I am familiar with this and I would be able to explain this well
## ST092Q06NA
## 1: I know something about this and could explain the general issue
## 2: I know something about this and could explain the general issue
## 3: I am familiar with this and I would be able to explain this well
## 4: I know something about this and could explain the general issue
## 5: I know something about this and could explain the general issue
## ---
## 3193: I know something about this and could explain the general issue
## 3194: I know something about this and could explain the general issue
## 3195: I am familiar with this and I would be able to explain this well
## 3196: I am familiar with this and I would be able to explain this well
## 3197: I know something about this and could explain the general issue
## ST092Q08NA
## 1: I know something about this and could explain the general issue
## 2: I know something about this and could explain the general issue
## 3: I know something about this and could explain the general issue
## 4: I know something about this and could explain the general issue
## 5: I have heard about this but I would not be able to explain what it is really about
## ---
## 3193: I know something about this and could explain the general issue
## 3194: I know something about this and could explain the general issue
## 3195: I am familiar with this and I would be able to explain this well
## 3196: I am familiar with this and I would be able to explain this well
## 3197: I know something about this and could explain the general issue
## ST092Q09NA
## 1: I know something about this and could explain the general issue
## 2: I know something about this and could explain the general issue
## 3: I know something about this and could explain the general issue
## 4: I know something about this and could explain the general issue
## 5: I have heard about this but I would not be able to explain what it is really about
## ---
## 3193: I am familiar with this and I would be able to explain this well
## 3194: I have heard about this but I would not be able to explain what it is really about
## 3195: I am familiar with this and I would be able to explain this well
## 3196: I am familiar with this and I would be able to explain this well
## 3197: I am familiar with this and I would be able to explain this well
## ST093Q01TA ST093Q03TA ST093Q04TA
## 1: Get worse Get worse Get worse
## 2: Get worse Get worse Get worse
## 3: Get worse Get worse Stay about the same
## 4: Get worse Get worse Get worse
## 5: Get worse Stay about the same Get worse
## ---
## 3193: Get worse Get worse Get worse
## 3194: Stay about the same Stay about the same Get worse
## 3195: Get worse Get worse Get worse
## 3196: Improve Get worse Stay about the same
## 3197: Stay about the same Get worse Get worse
## ST093Q05TA ST093Q06TA ST093Q07NA
## 1: Stay about the same Stay about the same Stay about the same
## 2: Stay about the same Stay about the same Stay about the same
## 3: Stay about the same Improve Improve
## 4: Stay about the same Get worse Get worse
## 5: Get worse Get worse Get worse
## ---
## 3193: Get worse Get worse Get worse
## 3194: Stay about the same Stay about the same Get worse
## 3195: Get worse Improve Get worse
## 3196: Stay about the same Improve Improve
## 3197: <NA> Improve Get worse
## ST093Q08NA ST094Q01NA ST094Q02NA ST094Q03NA
## 1: Stay about the same Disagree Disagree Disagree
## 2: Stay about the same Strongly disagree Strongly disagree Strongly disagree
## 3: Stay about the same Disagree Strongly disagree Strongly disagree
## 4: Get worse Disagree Disagree Disagree
## 5: Improve Strongly agree Disagree Disagree
## ---
## 3193: Get worse Strongly agree Agree Agree
## 3194: Get worse Strongly agree Strongly agree Agree
## 3195: Stay about the same Disagree <NA> Strongly disagree
## 3196: Get worse Agree Agree Disagree
## 3197: Stay about the same Disagree Agree Agree
## ST094Q04NA ST094Q05NA ST095Q04NA ST095Q07NA
## 1: Disagree Disagree Not interested Hardly interested
## 2: Strongly disagree Strongly disagree Interested Not interested
## 3: Strongly disagree Strongly disagree Hardly interested Hardly interested
## 4: Disagree Disagree Interested Interested
## 5: Agree Strongly agree Hardly interested Hardly interested
## ---
## 3193: Agree Agree Highly interested Interested
## 3194: Agree Strongly agree Interested Hardly interested
## 3195: Strongly disagree Strongly disagree Hardly interested Not interested
## 3196: Disagree Agree Hardly interested Not interested
## 3197: Disagree Strongly agree Interested Interested
## ST095Q08NA ST095Q13NA ST095Q15NA
## 1: Not interested Highly interested Interested
## 2: Not interested Not interested Not interested
## 3: Hardly interested I don't know what this is Interested
## 4: Not interested Highly interested Interested
## 5: Interested Interested Highly interested
## ---
## 3193: Interested Highly interested Highly interested
## 3194: Hardly interested Interested Interested
## 3195: Hardly interested Hardly interested Interested
## 3196: Not interested Interested Highly interested
## 3197: Highly interested Highly interested Highly interested
## ST113Q01TA ST113Q02TA ST113Q03TA ST113Q04TA
## 1: <NA> <NA> <NA> <NA>
## 2: Strongly Disagree Strongly Disagree Strongly Disagree Strongly Disagree
## 3: Agree Agree Strongly Disagree Strongly Disagree
## 4: Agree Disagree Strongly Disagree Disagree
## 5: Disagree Disagree Agree Agree
## ---
## 3193: Agree Agree Strongly Agree Strongly Agree
## 3194: Agree Disagree Disagree Disagree
## 3195: Disagree Agree Strongly Disagree Strongly Disagree
## 3196: Disagree Strongly Disagree Disagree Disagree
## 3197: Strongly Agree Strongly Agree Strongly Agree Strongly Agree
## ST129Q01TA
## 1: <NA>
## 2: I couldn't do this
## 3: I couldn't do this
## 4: I could do this with a bit of effort
## 5: I could do this with a bit of effort
## ---
## 3193: I could do this with a bit of effort
## 3194: I could do this with a bit of effort
## 3195: I could do this easily
## 3196: I could do this with a bit of effort
## 3197: I could do this with a bit of effort
## ST129Q02TA
## 1: <NA>
## 2: I couldn't do this
## 3: I could do this with a bit of effort
## 4: I could do this easily
## 5: I would struggle to do this on my own
## ---
## 3193: I could do this easily
## 3194: I could do this with a bit of effort
## 3195: I could do this easily
## 3196: I could do this easily
## 3197: I would struggle to do this on my own
## ST129Q03TA
## 1: <NA>
## 2: I would struggle to do this on my own
## 3: I couldn't do this
## 4: I would struggle to do this on my own
## 5: I would struggle to do this on my own
## ---
## 3193: I could do this with a bit of effort
## 3194: I would struggle to do this on my own
## 3195: I could do this easily
## 3196: I could do this with a bit of effort
## 3197: I could do this easily
## ST129Q04TA
## 1: <NA>
## 2: I couldn't do this
## 3: I would struggle to do this on my own
## 4: I couldn't do this
## 5: I would struggle to do this on my own
## ---
## 3193: I would struggle to do this on my own
## 3194: I would struggle to do this on my own
## 3195: I could do this easily
## 3196: I could do this with a bit of effort
## 3197: I could do this easily
## ST129Q05TA
## 1: <NA>
## 2: I couldn't do this
## 3: I couldn't do this
## 4: I could do this with a bit of effort
## 5: I could do this with a bit of effort
## ---
## 3193: I could do this with a bit of effort
## 3194: I would struggle to do this on my own
## 3195: <NA>
## 3196: I could do this easily
## 3197: I could do this with a bit of effort
## ST129Q06TA
## 1: <NA>
## 2: I couldn't do this
## 3: I couldn't do this
## 4: I could do this easily
## 5: I would struggle to do this on my own
## ---
## 3193: I couldn't do this
## 3194: I would struggle to do this on my own
## 3195: I could do this easily
## 3196: I could do this with a bit of effort
## 3197: I could do this easily
## ST129Q07TA
## 1: <NA>
## 2: I couldn't do this
## 3: I couldn't do this
## 4: I would struggle to do this on my own
## 5: I would struggle to do this on my own
## ---
## 3193: I couldn't do this
## 3194: I would struggle to do this on my own
## 3195: I could do this with a bit of effort
## 3196: I could do this with a bit of effort
## 3197: I could do this with a bit of effort
## ST129Q08TA ST131Q01NA ST131Q03NA
## 1: <NA> <NA> <NA>
## 2: I couldn't do this Strongly agree Agree
## 3: I couldn't do this Strongly agree Disagree
## 4: I couldn't do this Strongly agree Strongly agree
## 5: I would struggle to do this on my own Disagree Strongly agree
## ---
## 3193: I would struggle to do this on my own Agree Agree
## 3194: I would struggle to do this on my own Agree Disagree
## 3195: I could do this with a bit of effort Disagree Agree
## 3196: I could do this easily Strongly agree Agree
## 3197: I would struggle to do this on my own Strongly agree Strongly agree
## ST131Q04NA ST131Q06NA ST131Q08NA ST131Q11NA
## 1: <NA> <NA> <NA> <NA>
## 2: Agree Disagree Disagree <NA>
## 3: Strongly agree Strongly agree Agree Agree
## 4: Strongly agree Agree Agree Agree
## 5: Agree Strongly agree Agree Agree
## ---
## 3193: Agree Disagree Disagree Disagree
## 3194: Agree Agree Agree Disagree
## 3195: Agree Disagree Disagree Disagree
## 3196: Agree Disagree Strongly agree Strongly agree
## 3197: Strongly agree Strongly agree Strongly agree Strongly agree
## ST146Q01TA ST146Q02TA ST146Q03TA
## 1: <NA> <NA> <NA>
## 2: Never or hardly ever Never or hardly ever Never or hardly ever
## 3: Never or hardly ever Never or hardly ever Never or hardly ever
## 4: Never or hardly ever Never or hardly ever Never or hardly ever
## 5: Never or hardly ever Never or hardly ever Never or hardly ever
## ---
## 3193: Sometimes Sometimes Sometimes
## 3194: Sometimes Sometimes Sometimes
## 3195: Sometimes Never or hardly ever Never or hardly ever
## 3196: Sometimes Never or hardly ever Sometimes
## 3197: Never or hardly ever Never or hardly ever Sometimes
## ST146Q04TA ST146Q05TA ST146Q06NA
## 1: <NA> <NA> <NA>
## 2: Never or hardly ever Never or hardly ever Never or hardly ever
## 3: Never or hardly ever Never or hardly ever Never or hardly ever
## 4: Never or hardly ever Never or hardly ever Never or hardly ever
## 5: Sometimes Never or hardly ever Never or hardly ever
## ---
## 3193: Sometimes Never or hardly ever Never or hardly ever
## 3194: Sometimes Never or hardly ever Never or hardly ever
## 3195: Sometimes Never or hardly ever Never or hardly ever
## 3196: Sometimes Sometimes Never or hardly ever
## 3197: Sometimes Never or hardly ever Never or hardly ever
## ST146Q07NA ST146Q08NA ST146Q09NA ST076Q01NA
## 1: <NA> <NA> <NA> <NA>
## 2: Never or hardly ever Never or hardly ever Never or hardly ever Yes
## 3: Never or hardly ever Never or hardly ever Never or hardly ever Yes
## 4: Never or hardly ever Sometimes Sometimes No
## 5: Never or hardly ever Never or hardly ever Never or hardly ever Yes
## ---
## 3193: Never or hardly ever Sometimes Sometimes Yes
## 3194: Never or hardly ever Never or hardly ever Never or hardly ever No
## 3195: Never or hardly ever <NA> Regularly No
## 3196: Never or hardly ever Never or hardly ever Regularly No
## 3197: Never or hardly ever Sometimes Never or hardly ever Yes
## ST076Q02NA ST076Q03NA ST076Q04NA ST076Q05NA ST076Q06NA ST076Q07NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: No No No No No No
## 3: No No No Yes No No
## 4: No No No Yes No No
## 5: No No Yes No No No
## ---
## 3193: No No Yes No No No
## 3194: No No No Yes No No
## 3195: No No No Yes No No
## 3196: No Yes Yes Yes Yes Yes
## 3197: Yes Yes Yes Yes Yes No
## ST076Q08NA ST076Q09NA ST076Q10NA ST076Q11NA ST078Q01NA ST078Q02NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: Yes Yes No No <NA> <NA>
## 3: Yes No No No Yes Yes
## 4: Yes No No No Yes Yes
## 5: Yes No No No Yes Yes
## ---
## 3193: Yes No No No Yes Yes
## 3194: No No No No Yes Yes
## 3195: No No No Yes Yes No
## 3196: Yes Yes No No Yes No
## 3197: Yes Yes No Yes Yes Yes
## ST078Q03NA ST078Q04NA ST078Q05NA ST078Q06NA ST078Q07NA ST078Q08NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: Yes Yes Yes No Yes Yes
## 4: Yes Yes Yes No Yes Yes
## 5: Yes No Yes No No Yes
## ---
## 3193: Yes Yes Yes No Yes Yes
## 3194: Yes Yes Yes No Yes Yes
## 3195: Yes No Yes No No Yes
## 3196: Yes Yes Yes Yes Yes Yes
## 3197: No Yes Yes Yes No Yes
## ST078Q09NA ST078Q10NA ST078Q11NA ST065Class IC001Q01TA IC001Q02TA
## 1: <NA> <NA> <NA> Chemistry <NA> <NA>
## 2: <NA> <NA> <NA> Chemistry <NA> <NA>
## 3: Yes No No Chemistry <NA> <NA>
## 4: Yes No Yes Chemistry <NA> <NA>
## 5: Yes No Yes Chemistry <NA> <NA>
## ---
## 3193: Yes No Yes Physics <NA> <NA>
## 3194: Yes No Yes Biology <NA> <NA>
## 3195: No No Yes Biology <NA> <NA>
## 3196: Yes Yes No Biology <NA> <NA>
## 3197: Yes No Yes Biology <NA> <NA>
## IC001Q03TA IC001Q04TA IC001Q05TA IC001Q06TA IC001Q07TA IC001Q08TA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## IC001Q09TA IC001Q10TA IC001Q11TA IC009Q01TA IC009Q02TA IC009Q03TA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## IC009Q05NA IC009Q06NA IC009Q07NA IC009Q08TA IC009Q09TA IC009Q10NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## IC009Q11NA IC002Q01NA IC003Q01TA IC004Q01TA IC005Q01TA IC006Q01TA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## IC007Q01TA IC008Q01TA IC008Q02TA
## 1: <NA> Never or hardly ever Never or hardly ever
## 2: <NA> Never or hardly ever Never or hardly ever
## 3: <NA> Never or hardly ever Never or hardly ever
## 4: <NA> Never or hardly ever Never or hardly ever
## 5: <NA> Once or twice a month Never or hardly ever
## ---
## 3193: <NA> Never or hardly ever Never or hardly ever
## 3194: <NA> Once or twice a month Never or hardly ever
## 3195: <NA> Never or hardly ever Never or hardly ever
## 3196: <NA> Almost every day Almost every day
## 3197: <NA> Every day Almost every day
## IC008Q03TA IC008Q04TA IC008Q05TA
## 1: Once or twice a week Every day Every day
## 2: Once or twice a week Every day Every day
## 3: <NA> Almost every day Every day
## 4: Almost every day Every day Every day
## 5: Once or twice a week Once or twice a month Every day
## ---
## 3193: Never or hardly ever Never or hardly ever Once or twice a week
## 3194: Almost every day Never or hardly ever Almost every day
## 3195: Once or twice a month Never or hardly ever Every day
## 3196: Once or twice a month Every day Once or twice a week
## 3197: Almost every day Never or hardly ever Once or twice a week
## IC008Q07NA IC008Q08TA IC008Q09TA
## 1: Never or hardly ever Every day Never or hardly ever
## 2: Never or hardly ever Almost every day Once or twice a month
## 3: Never or hardly ever Every day Once or twice a month
## 4: Never or hardly ever Every day Once or twice a week
## 5: Never or hardly ever Every day Never or hardly ever
## ---
## 3193: Never or hardly ever Once or twice a month Never or hardly ever
## 3194: Never or hardly ever Once or twice a week Once or twice a week
## 3195: Never or hardly ever Almost every day Once or twice a week
## 3196: Never or hardly ever Every day Almost every day
## 3197: Never or hardly ever Every day Once or twice a week
## IC008Q10TA IC008Q11TA IC008Q12TA
## 1: Once or twice a week Once or twice a week Never or hardly ever
## 2: Never or hardly ever Never or hardly ever Never or hardly ever
## 3: Almost every day Every day Almost every day
## 4: Almost every day Almost every day Every day
## 5: Every day Almost every day Never or hardly ever
## ---
## 3193: Once or twice a month Once or twice a week Never or hardly ever
## 3194: Once or twice a week Once or twice a month Never or hardly ever
## 3195: Once or twice a week Never or hardly ever Never or hardly ever
## 3196: Almost every day Every day Once or twice a week
## 3197: Every day Almost every day Once or twice a month
## IC008Q13NA IC010Q01TA IC010Q02NA
## 1: Never or hardly ever Once or twice a week Once or twice a week
## 2: Once or twice a month Once or twice a month Once or twice a month
## 3: Almost every day Almost every day Almost every day
## 4: Almost every day Once or twice a week Once or twice a month
## 5: Once or twice a month Almost every day Once or twice a week
## ---
## 3193: Once or twice a month Once or twice a week Once or twice a month
## 3194: Once or twice a month Once or twice a month Once or twice a month
## 3195: Almost every day Once or twice a month Never or hardly ever
## 3196: Once or twice a month Almost every day Once or twice a month
## 3197: Once or twice a month Almost every day Once or twice a week
## IC010Q03TA IC010Q04TA IC010Q05NA
## 1: Once or twice a month Never or hardly ever Once or twice a month
## 2: Never or hardly ever Never or hardly ever Almost every day
## 3: Almost every day Once or twice a month Every day
## 4: Never or hardly ever Never or hardly ever Every day
## 5: Never or hardly ever Never or hardly ever Once or twice a week
## ---
## 3193: Never or hardly ever Never or hardly ever Once or twice a week
## 3194: Almost every day Never or hardly ever Never or hardly ever
## 3195: Never or hardly ever Never or hardly ever Every day
## 3196: Once or twice a week Almost every day Almost every day
## 3197: Almost every day Never or hardly ever Every day
## IC010Q06NA IC010Q07TA IC010Q08TA
## 1: Never or hardly ever Never or hardly ever Never or hardly ever
## 2: Never or hardly ever Never or hardly ever Never or hardly ever
## 3: Once or twice a month Once or twice a month Once or twice a month
## 4: Never or hardly ever Once or twice a month Once or twice a month
## 5: Never or hardly ever <NA> Never or hardly ever
## ---
## 3193: Never or hardly ever Never or hardly ever Never or hardly ever
## 3194: Never or hardly ever Never or hardly ever Never or hardly ever
## 3195: Never or hardly ever Never or hardly ever Never or hardly ever
## 3196: Never or hardly ever Never or hardly ever Never or hardly ever
## 3197: Never or hardly ever Never or hardly ever Never or hardly ever
## IC010Q09NA IC010Q10NA IC010Q11NA
## 1: Never or hardly ever Never or hardly ever Never or hardly ever
## 2: Never or hardly ever Never or hardly ever Never or hardly ever
## 3: Once or twice a month Once or twice a month Never or hardly ever
## 4: Once or twice a week Once or twice a week Once or twice a month
## 5: Once or twice a week Never or hardly ever Never or hardly ever
## ---
## 3193: Once or twice a month Once or twice a month Never or hardly ever
## 3194: Once or twice a month Once or twice a month Never or hardly ever
## 3195: Once or twice a month Once or twice a week Never or hardly ever
## 3196: Once or twice a week Once or twice a week Never or hardly ever
## 3197: Once or twice a week Never or hardly ever Never or hardly ever
## IC010Q12NA IC011Q01TA IC011Q02TA
## 1: Never or hardly ever Never or hardly ever Never or hardly ever
## 2: Never or hardly ever Never or hardly ever Never or hardly ever
## 3: Never or hardly ever Never or hardly ever Never or hardly ever
## 4: Never or hardly ever Never or hardly ever Never or hardly ever
## 5: Never or hardly ever Never or hardly ever Never or hardly ever
## ---
## 3193: Never or hardly ever Never or hardly ever Never or hardly ever
## 3194: Never or hardly ever Never or hardly ever Once or twice a month
## 3195: Never or hardly ever Never or hardly ever Never or hardly ever
## 3196: Never or hardly ever Once or twice a week Never or hardly ever
## 3197: Never or hardly ever Never or hardly ever Never or hardly ever
## IC011Q03TA IC011Q04TA IC011Q05TA
## 1: Never or hardly ever Never or hardly ever Never or hardly ever
## 2: Once or twice a month Once or twice a month Never or hardly ever
## 3: Once or twice a month Once or twice a month Never or hardly ever
## 4: Once or twice a week Once or twice a month Never or hardly ever
## 5: Once or twice a month Never or hardly ever Never or hardly ever
## ---
## 3193: Once or twice a month Never or hardly ever Never or hardly ever
## 3194: Once or twice a month Never or hardly ever Never or hardly ever
## 3195: Once or twice a month Once or twice a month Never or hardly ever
## 3196: Once or twice a month Never or hardly ever Never or hardly ever
## 3197: Once or twice a month Never or hardly ever Never or hardly ever
## IC011Q06TA IC011Q07TA IC011Q08TA
## 1: Never or hardly ever Never or hardly ever Never or hardly ever
## 2: Never or hardly ever Once or twice a month Never or hardly ever
## 3: Never or hardly ever Never or hardly ever Never or hardly ever
## 4: Never or hardly ever Never or hardly ever Never or hardly ever
## 5: Never or hardly ever Never or hardly ever Never or hardly ever
## ---
## 3193: Never or hardly ever <NA> Never or hardly ever
## 3194: Never or hardly ever Never or hardly ever Never or hardly ever
## 3195: Never or hardly ever Never or hardly ever Never or hardly ever
## 3196: Never or hardly ever Never or hardly ever Never or hardly ever
## 3197: Never or hardly ever Never or hardly ever Never or hardly ever
## IC011Q09TA IC013Q01NA IC013Q04NA IC013Q05NA
## 1: Never or hardly ever Agree Strongly agree Agree
## 2: Never or hardly ever Strongly agree Strongly agree Strongly agree
## 3: Never or hardly ever Strongly agree Strongly agree Strongly agree
## 4: Once or twice a month Agree Strongly agree Strongly agree
## 5: Never or hardly ever Disagree Agree Agree
## ---
## 3193: Once or twice a month Disagree <NA> <NA>
## 3194: Once or twice a month Disagree Agree Agree
## 3195: Never or hardly ever Disagree Agree Agree
## 3196: Never or hardly ever Strongly agree Strongly agree Strongly agree
## 3197: Once or twice a month Strongly agree Strongly agree Strongly agree
## IC013Q11NA IC013Q12NA IC013Q13NA IC014Q03NA
## 1: Disagree Agree Strongly agree Disagree
## 2: Agree Agree Agree Disagree
## 3: Disagree Agree Agree Disagree
## 4: Strongly agree Strongly agree Strongly agree Agree
## 5: Disagree Strongly agree Agree Strongly disagree
## ---
## 3193: <NA> <NA> <NA> <NA>
## 3194: Disagree Strongly disagree Disagree Strongly disagree
## 3195: Disagree Strongly disagree Agree Disagree
## 3196: Strongly agree Strongly agree Strongly agree Strongly agree
## 3197: Strongly agree Strongly agree Strongly agree Strongly agree
## IC014Q04NA IC014Q06NA IC014Q08NA IC014Q09NA
## 1: Strongly disagree Agree Agree Agree
## 2: Agree Strongly agree Disagree Disagree
## 3: Strongly disagree Disagree Disagree Strongly disagree
## 4: Disagree Strongly agree Disagree Disagree
## 5: Strongly disagree Strongly agree Disagree Strongly disagree
## ---
## 3193: <NA> <NA> <NA> <NA>
## 3194: Agree Disagree Agree Disagree
## 3195: Agree Agree Agree Agree
## 3196: Strongly agree Strongly agree Agree Agree
## 3197: Strongly agree Strongly agree Strongly agree Strongly agree
## IC015Q02NA IC015Q03NA IC015Q05NA IC015Q07NA
## 1: Agree Agree Agree Strongly agree
## 2: Strongly disagree Strongly disagree Agree Disagree
## 3: Strongly disagree Disagree Disagree Strongly disagree
## 4: Agree Disagree Strongly agree Disagree
## 5: Disagree Disagree Agree Agree
## ---
## 3193: <NA> <NA> <NA> <NA>
## 3194: Disagree Disagree Agree Disagree
## 3195: Strongly agree Agree Strongly agree Disagree
## 3196: Strongly agree Strongly agree Strongly agree Agree
## 3197: Strongly agree Strongly agree Strongly agree Strongly agree
## IC015Q09NA IC016Q01NA IC016Q02NA IC016Q04NA
## 1: Agree Strongly disagree Disagree Strongly disagree
## 2: Disagree Disagree Agree Strongly disagree
## 3: Strongly disagree Agree Strongly disagree Strongly disagree
## 4: Disagree Strongly agree Strongly disagree Strongly disagree
## 5: Agree Agree Disagree Disagree
## ---
## 3193: <NA> <NA> <NA> <NA>
## 3194: Agree Disagree Disagree Strongly disagree
## 3195: Agree Strongly disagree Disagree Agree
## 3196: Strongly agree Disagree Strongly agree Strongly agree
## 3197: Strongly agree Agree Strongly agree Disagree
## IC016Q05NA IC016Q07NA EC001Q01NA EC001Q02NA EC001Q03NA
## 1: Strongly agree Strongly disagree NA NA NA
## 2: Strongly disagree Strongly disagree 3 6 NA
## 3: Strongly disagree Disagree 0 1 0
## 4: Strongly disagree Strongly disagree NA 1 1
## 5: Agree Disagree 0 0 0
## ---
## 3193: <NA> <NA> NA NA NA
## 3194: Strongly disagree Strongly disagree 0 0 0
## 3195: Disagree Disagree NA NA NA
## 3196: Strongly agree Strongly agree NA 2 NA
## 3197: Agree Strongly agree NA NA NA
## EC001Q04NA EC001Q05NA EC001Q06NA EC001Q07NA EC001Q08NA EC001Q09NA
## 1: NA NA NA NA NA NA
## 2: NA NA NA NA NA NA
## 3: 0 0 1 0 4 0
## 4: 2 NA NA 4 NA NA
## 5: 0 0 0 0 0 0
## ---
## 3193: NA NA NA NA NA NA
## 3194: 0 0 0 0 0 0
## 3195: NA NA NA 2 NA 6
## 3196: NA NA NA NA NA NA
## 3197: 1 NA NA NA NA NA
## EC001Q10NA EC003Q01NA EC003Q02NA EC003Q03NA EC003Q04NA EC003Q05NA
## 1: NA <NA> <NA> <NA> <NA> <NA>
## 2: NA <NA> <NA> <NA> <NA> <NA>
## 3: 0 <NA> <NA> <NA> <NA> <NA>
## 4: 1 <NA> <NA> <NA> <NA> <NA>
## 5: 0 <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: NA <NA> <NA> <NA> <NA> <NA>
## 3194: 0 <NA> <NA> <NA> <NA> <NA>
## 3195: NA <NA> <NA> <NA> <NA> <NA>
## 3196: NA <NA> <NA> <NA> <NA> <NA>
## 3197: NA <NA> <NA> <NA> <NA> <NA>
## EC003Q06NA EC004Q01NA EC004Q02NA EC005Q01NA EC005Q02NA EC005Q03NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC005Q04NA EC005Q05NA EC005Q06NA EC005Q07NA EC005Q08NA EC007Q01NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC007Q02NA EC008Q01NA EC008Q02NA EC008Q03NA EC008Q04NA EC009Q03NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC009Q07NA EC009Q10NA EC009Q12NA EC009Q13NA EC009Q14NA EC010Q04NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC010Q06NA EC010Q07NA EC010Q08NA EC010Q09NA EC010Q10NA EC010Q11NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC010Q12NA EC011Q01NA EC011Q02NA EC011Q03NA EC011Q04NA EC011Q05NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC012Q01NA EC012Q02NA EC012Q03NA EC012Q04NA EC012Q05NA EC012Q06NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: Not checked Checked Not checked Not checked Not checked Not checked
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC012Q07NA EC012Q08NA EC012Q09NA EC012Q10NA EC012Q11NA EC012Q12NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: Checked Checked Not checked Not checked Not checked Checked
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC013Q01NA EC013Q02NA EC013Q03NA EC013Q04NA EC013Q05NA EC013Q06NA
## 1: Not checked Not checked Not checked Not checked Not checked Not checked
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: Checked Not checked Not checked Checked Not checked Checked
## 4: Checked Checked Checked Checked Not checked Not checked
## 5: Checked Not checked Checked Checked Not checked Checked
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: Checked Not checked Not checked Not checked Not checked Checked
## 3195: Checked Not checked Not checked Not checked Not checked Not checked
## 3196: Checked Checked Checked Checked Checked Checked
## 3197: Checked Not checked Checked Checked Checked Not checked
## EC013Q07NA EC013Q08NA EC013Q09NA EC013Q10NA EC013Q11NA EC013Q12NA
## 1: Not checked Not checked Not checked Not checked Not checked Checked
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: Not checked Not checked Not checked Not checked Not checked Not checked
## 4: Not checked Not checked Not checked Checked Checked Not checked
## 5: Not checked Checked Not checked Checked Checked Checked
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: Not checked Not checked Not checked Checked Not checked Not checked
## 3195: Not checked Not checked Not checked Not checked Not checked Not checked
## 3196: Not checked Checked Not checked Checked Checked Checked
## 3197: Not checked Checked Checked Not checked Not checked Checked
## EC013Q13NA EC014Q01NA EC014Q02NA EC015Q01NA EC015Q02NA EC015Q03NA
## 1: Checked <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: Not checked <NA> <NA> <NA> <NA> <NA>
## 4: Not checked <NA> <NA> <NA> <NA> <NA>
## 5: Checked <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: Not checked <NA> <NA> <NA> <NA> <NA>
## 3195: Not checked <NA> <NA> <NA> <NA> <NA>
## 3196: Checked <NA> <NA> <NA> <NA> <NA>
## 3197: Checked <NA> <NA> <NA> <NA> <NA>
## EC015Q04NA EC015Q05NA EC015Q06NA EC015Q07NA EC015Q08NA EC017Q01NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC017Q02NA EC018Q01NA EC018Q02NA EC018Q03NA EC018Q04NA EC019Q03NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC019Q07NA EC019Q10NA EC019Q12NA EC019Q13NA EC019Q14NA EC020Q04NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC020Q06NA EC020Q07NA EC020Q08NA EC020Q09NA EC020Q10NA EC020Q11NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC020Q12NA EC021Q01NA EC021Q02NA EC021Q03NA EC021Q04NA EC021Q05NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC022Q01NA EC022Q02NA EC022Q03NA EC022Q04NA EC022Q05NA EC022Q06NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC022Q07NA EC022Q08NA EC022Q09NA EC022Q10NA EC022Q11NA EC022Q12NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC023Q01NA EC023Q02NA EC023Q03NA EC023Q04NA EC023Q05NA EC023Q06NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC023Q07NA EC023Q08NA EC023Q09NA EC023Q10NA EC023Q11NA EC023Q12NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC023Q13NA EC024Q01NA EC024Q02NA EC024Q03NA EC024Q04NA EC024Q05NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC024Q06NA EC024Q07NA EC024Q08NA EC026Q01NA EC026Q02NA EC027Q01NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC027Q02NA EC027Q03NA EC027Q04NA EC028Q01NA EC028Q02NA EC028Q03NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: <NA> <NA> <NA> <NA> <NA> <NA>
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## EC029Q01NA EC030Q01NA EC030Q02NA EC030Q03NA EC030Q04NA EC030Q05NA
## 1: NA No Yes Yes No No
## 2: NA Yes Yes Yes Yes Yes
## 3: NA Yes No No No Yes
## 4: NA Yes No Yes No No
## 5: NA Yes No No No No
## ---
## 3193: NA <NA> <NA> <NA> <NA> <NA>
## 3194: NA Yes Yes No No Yes
## 3195: NA No No No No No
## 3196: NA Yes Yes Yes No No
## 3197: NA Yes No Yes No No
## EC030Q06NA EC030Q07NA EC031Q01TA
## 1: No Yes No, I attended all of <ISCED 1> at the same school.
## 2: Yes Yes No, I attended all of <ISCED 1> at the same school.
## 3: Yes No No, I attended all of <ISCED 1> at the same school.
## 4: Yes Yes No, I attended all of <ISCED 1> at the same school.
## 5: No Yes No, I attended all of <ISCED 1> at the same school.
## ---
## 3193: <NA> <NA> <NA>
## 3194: No No Yes, I changed schools twice or more.
## 3195: Yes No No, I attended all of <ISCED 1> at the same school.
## 3196: Yes No Yes, I changed schools once.
## 3197: Yes No No, I attended all of <ISCED 1> at the same school.
## EC032Q01TA EC033Q01NA
## 1: No, I attended all of <ISCED 2> at the same school. <NA>
## 2: Yes, I changed schools once. <NA>
## 3: No, I attended all of <ISCED 2> at the same school. <NA>
## 4: No, I attended all of <ISCED 2> at the same school. <NA>
## 5: No, I attended all of <ISCED 2> at the same school. <NA>
## ---
## 3193: <NA> <NA>
## 3194: No, I attended all of <ISCED 2> at the same school. <NA>
## 3195: Yes, I changed schools once. <NA>
## 3196: Yes, I changed schools once. <NA>
## 3197: No, I attended all of <ISCED 2> at the same school. <NA>
## PA001Q01TA PA001Q02TA PA001Q03TA PA002Q01TA PA002Q02TA PA002Q03TA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: Checked Not checked Not checked Never Never Never
## 3: Checked Not checked Not checked Never Never Never
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: Checked Not checked Not checked Sometimes Never Sometimes
## ---
## 3193: Checked Not checked Not checked Sometimes Sometimes Never
## 3194: Checked Not checked Not checked Sometimes Never Never
## 3195: Checked Not checked Not checked Sometimes Sometimes Never
## 3196: Not checked Not checked Not checked Sometimes Sometimes Never
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA002Q04TA PA002Q05TA PA002Q06NA PA002Q07NA PA002Q08NA PA002Q09NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: Never Never Never Never Never Never
## 3: Never Never Very Often Never Never Never
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: Sometimes Never Very Often Sometimes Never Sometimes
## ---
## 3193: Never Never Sometimes Never Never Sometimes
## 3194: Sometimes Never Sometimes Never Sometimes Sometimes
## 3195: Sometimes Never Sometimes Never Sometimes Sometimes
## 3196: Sometimes Never Never Never Never Sometimes
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA002Q10NA PA003Q01TA PA003Q02TA
## 1: <NA> <NA> <NA>
## 2: Never Once or twice a week Once or twice a week
## 3: Never Once or twice a week Every day or almost every day
## 4: <NA> <NA> <NA>
## 5: Sometimes Once or twice a week Every day or almost every day
## ---
## 3193: Sometimes Once or twice a month Every day or almost every day
## 3194: Sometimes Once or twice a week Once or twice a week
## 3195: Sometimes Every day or almost every day Every day or almost every day
## 3196: Never Every day or almost every day Every day or almost every day
## 3197: <NA> <NA> <NA>
## PA003Q03TA PA003Q04NA PA003Q05NA
## 1: <NA> <NA> <NA>
## 2: Every day or almost every day Never or hardly ever Once or twice a week
## 3: Every day or almost every day <NA> Once or twice a week
## 4: <NA> <NA> <NA>
## 5: Every day or almost every day Once or twice a year Once or twice a week
## ---
## 3193: Every day or almost every day Once or twice a month Once or twice a month
## 3194: Every day or almost every day Once or twice a month Once or twice a year
## 3195: Every day or almost every day Once or twice a month Once or twice a month
## 3196: Every day or almost every day Once or twice a week Once or twice a week
## 3197: <NA> <NA> <NA>
## PA003Q06NA PA003Q07NA PA003Q08NA
## 1: <NA> <NA> <NA>
## 2: Never or hardly ever Never or hardly ever Never or hardly ever
## 3: Never or hardly ever Once or twice a month Once or twice a year
## 4: <NA> <NA> <NA>
## 5: Never or hardly ever Once or twice a year Once or twice a month
## ---
## 3193: Once or twice a year Once or twice a month Never or hardly ever
## 3194: Once or twice a year Once or twice a year Once or twice a year
## 3195: Never or hardly ever Once or twice a month Never or hardly ever
## 3196: Once or twice a month Once or twice a month Once or twice a month
## 3197: <NA> <NA> <NA>
## PA004Q01NA PA004Q02NA PA004Q03NA PA004Q04NA
## 1: <NA> <NA> <NA> <NA>
## 2: Agree Agree Agree Strongly agree
## 3: Strongly agree Strongly agree Strongly agree Strongly agree
## 4: <NA> <NA> <NA> <NA>
## 5: Strongly agree Strongly agree Strongly agree Strongly agree
## ---
## 3193: Agree Strongly agree Strongly agree Strongly agree
## 3194: Strongly agree Strongly agree Strongly agree Strongly agree
## 3195: Disagree Agree Agree Agree
## 3196: Strongly agree Strongly agree Strongly agree Strongly agree
## 3197: <NA> <NA> <NA> <NA>
## PA005Q01TA
## 1: <NA>
## 2: There is one other school in this area that competes with the school my child is currently attending.
## 3: There is one other school in this area that competes with the school my child is currently attending.
## 4: <NA>
## 5: There are no other schools in this area that compete with the school my child is currently attending.
## ---
## 3193: There are two or more other schools in this area that compete with the school my child is currently attending.
## 3194: There are two or more other schools in this area that compete with the school my child is currently attending.
## 3195: There are no other schools in this area that compete with the school my child is currently attending.
## 3196: There are no other schools in this area that compete with the school my child is currently attending.
## 3197: <NA>
## PA006Q01TA PA006Q02TA PA006Q03TA PA006Q04TA
## 1: <NA> <NA> <NA> <NA>
## 2: Not important Important Important Not important
## 3: Important Very important Somewhat important Somewhat important
## 4: <NA> <NA> <NA> <NA>
## 5: Very important Very important Important Not important
## ---
## 3193: Important Important Important Somewhat important
## 3194: Very important Very important Very important Not important
## 3195: Somewhat important Important Somewhat important Not important
## 3196: Very important Very important Very important Not important
## 3197: <NA> <NA> <NA> <NA>
## PA006Q05TA PA006Q06TA PA006Q07TA
## 1: <NA> <NA> <NA>
## 2: Not important Somewhat important Important
## 3: Not important Not important Somewhat important
## 4: <NA> <NA> <NA>
## 5: Not important Not important Very important
## ---
## 3193: Somewhat important Not important Somewhat important
## 3194: Somewhat important Not important Not important
## 3195: Not important Not important Not important
## 3196: <NA> Not important Very important
## 3197: <NA> <NA> <NA>
## PA006Q08TA PA006Q09TA PA006Q10TA PA006Q11TA
## 1: <NA> <NA> <NA> <NA>
## 2: Somewhat important Important Important Important
## 3: Not important Important Very important Very important
## 4: <NA> <NA> <NA> <NA>
## 5: Very important Very important Very important Very important
## ---
## 3193: Important Very important Somewhat important Somewhat important
## 3194: Not important Very important Very important Very important
## 3195: Not important Important Important Important
## 3196: Important Very important Important Very important
## 3197: <NA> <NA> <NA> <NA>
## PA007Q01TA PA007Q02TA PA007Q03TA PA007Q04TA
## 1: <NA> <NA> <NA> <NA>
## 2: Agree Strongly agree Agree Agree
## 3: Agree Agree Agree Agree
## 4: <NA> <NA> <NA> <NA>
## 5: Agree Strongly agree Disagree Agree
## ---
## 3193: Disagree Disagree Disagree Disagree
## 3194: Disagree Strongly disagree Strongly disagree Strongly disagree
## 3195: Disagree Disagree Disagree Disagree
## 3196: Agree Agree Disagree Agree
## 3197: <NA> <NA> <NA> <NA>
## PA007Q05TA PA007Q06TA PA007Q07TA PA007Q09NA
## 1: <NA> <NA> <NA> <NA>
## 2: Agree Agree Agree Agree
## 3: Agree Agree Strongly agree Agree
## 4: <NA> <NA> <NA> <NA>
## 5: Agree Agree Agree Agree
## ---
## 3193: Disagree Disagree Disagree Disagree
## 3194: Strongly disagree Strongly disagree Strongly disagree Strongly disagree
## 3195: Disagree Strongly disagree Disagree Disagree
## 3196: Strongly agree Agree Agree Disagree
## 3197: <NA> <NA> <NA> <NA>
## PA007Q11NA PA007Q12NA PA007Q13NA PA007Q14NA
## 1: <NA> <NA> <NA> <NA>
## 2: Agree Agree Disagree Disagree
## 3: Agree Disagree Disagree Disagree
## 4: <NA> <NA> <NA> <NA>
## 5: Agree Disagree Strongly disagree Strongly disagree
## ---
## 3193: Strongly disagree Disagree Strongly disagree Disagree
## 3194: Strongly disagree Strongly disagree Strongly disagree Strongly disagree
## 3195: Disagree Agree Disagree Disagree
## 3196: Agree Disagree Disagree Strongly disagree
## 3197: <NA> <NA> <NA> <NA>
## PA007Q15NA PA008Q01TA PA008Q02TA PA008Q03TA PA008Q04TA PA008Q05TA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: Disagree Yes No Yes No No
## 3: Agree No No No No No
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: Disagree <NA> <NA> No Yes Yes
## ---
## 3193: Agree Yes Yes No No No
## 3194: Strongly disagree Yes Yes <NA> Yes No
## 3195: Disagree No No No No No
## 3196: Disagree Yes Yes No Yes No
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA008Q06NA PA008Q07NA PA008Q08NA PA008Q09NA PA008Q10NA
## 1: <NA> <NA> <NA> <NA> <NA>
## 2: No No Yes No No
## 3: No No Yes No No
## 4: <NA> <NA> <NA> <NA> <NA>
## 5: No No Yes No Yes
## ---
## 3193: Yes No Yes No Yes
## 3194: No No Yes No No
## 3195: No No Yes No No
## 3196: Not supported by school Yes Yes Yes Yes
## 3197: <NA> <NA> <NA> <NA> <NA>
## PA009Q01NA PA009Q02NA PA009Q03NA PA009Q04NA PA009Q05NA PA009Q06NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: No No No No No No
## 3: No No No No No No
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: No No No No No No
## ---
## 3193: No No No No No No
## 3194: Yes Yes Yes No No No
## 3195: Yes Yes No No No No
## 3196: No Yes Yes No No No
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA009Q08NA PA009Q09NA PA009Q10NA PA009Q11NA PA011Q01NA PA011Q02NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: No No No No 6 or more 6 or more
## 3: No No No No 6 or more 6 or more
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: No No No No 6 or more 6 or more
## ---
## 3193: No No No No 6 or more 6 or more
## 3194: No No No No 6 or more 1-2
## 3195: No Yes No No 6 or more 6 or more
## 3196: No No No No 3-5 3-5
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA011Q03NA PA014Q01NA PA018Q01NA PA018Q02NA PA018Q03NA PA019Q01NA
## 1: <NA> NA <NA> <NA> <NA> <NA>
## 2: 3-5 6 No No Yes <NA>
## 3: 3-5 6 No No Yes <NA>
## 4: <NA> NA <NA> <NA> <NA> <NA>
## 5: 3-5 6 <NA> No Yes Not checked
## ---
## 3193: 1-2 6 Yes No No Not checked
## 3194: 1-2 6 No No No <NA>
## 3195: 1-2 6 Yes <NA> <NA> Not checked
## 3196: 1-2 6 Yes No No Not checked
## 3197: <NA> NA <NA> <NA> <NA> <NA>
## PA019Q02NA PA019Q03NA PA019Q04NA PA019Q05NA PA019Q06NA PA019Q07NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: Not checked Not checked Not checked Not checked Not checked Not checked
## ---
## 3193: Not checked Not checked Checked Not checked Not checked Not checked
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: Not checked Not checked Checked Not checked Not checked Not checked
## 3196: Not checked Not checked Checked Not checked Not checked Not checked
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA019Q08NA PA020Q01NA PA020Q02NA PA020Q03NA PA020Q04NA PA021Q01NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: Not checked Not checked Not checked Not checked Not checked Not checked
## ---
## 3193: Not checked Not checked Not checked Not checked Checked Not checked
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: Not checked Not checked Checked Not checked Not checked Checked
## 3196: Not checked Not checked Not checked Not checked Checked Checked
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA021Q02NA PA021Q03NA PA021Q04NA
## 1: <NA> <NA> <NA>
## 2: <NA> <NA> <NA>
## 3: <NA> <NA> <NA>
## 4: <NA> <NA> <NA>
## 5: Not checked Not checked Not checked
## ---
## 3193: Not checked Checked Not checked
## 3194: <NA> <NA> <NA>
## 3195: Not checked Checked Not checked
## 3196: Not checked Not checked Not checked
## 3197: <NA> <NA> <NA>
## PA022Q01NA
## 1: <NA>
## 2: <NA>
## 3: <NA>
## 4: <NA>
## 5: <NA>
## ---
## 3193: We\\I wanted additional learning stimulation for the child (e. g., social, academic).
## 3194: <NA>
## 3195: We\\I wanted additional learning stimulation for the child (e. g., social, academic).
## 3196: We\\I wanted additional learning stimulation for the child (e. g., social, academic).
## 3197: <NA>
## PA023Q01NA PA023Q02NA PA023Q03NA PA023Q04NA PA023Q05NA PA023Q06NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: <NA> <NA> <NA> <NA> <NA> <NA>
## 3: <NA> <NA> <NA> <NA> <NA> <NA>
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: <NA> <NA> <NA> <NA> <NA> <NA>
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: Not checked Not checked Not checked Checked Not checked Not checked
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA023Q07NA PA023Q08NA
## 1: <NA> <NA>
## 2: <NA> <NA>
## 3: <NA> <NA>
## 4: <NA> <NA>
## 5: <NA> <NA>
## ---
## 3193: <NA> <NA>
## 3194: <NA> <NA>
## 3195: Not checked Not checked
## 3196: <NA> <NA>
## 3197: <NA> <NA>
## PA026Q01NA
## 1: <NA>
## 2: <NA>
## 3: <NA>
## 4: <NA>
## 5: <NA>
## ---
## 3193: <NA>
## 3194: <NA>
## 3195: We\\I wanted additional learning stimulation for the child (e. g., social, academic).
## 3196: <NA>
## 3197: <NA>
## PA027Q01NA PA027Q02NA PA027Q03NA PA027Q04NA PA027Q05NA PA027Q06NA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: Not checked Not checked Not checked Checked Not checked Not checked
## 3: Not checked Not checked Not checked Not checked Not checked Not checked
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: Not checked Not checked Not checked Checked Not checked Not checked
## ---
## 3193: <NA> <NA> <NA> <NA> <NA> <NA>
## 3194: <NA> <NA> <NA> <NA> <NA> <NA>
## 3195: Not checked Not checked Not checked Not checked Not checked Checked
## 3196: <NA> <NA> <NA> <NA> <NA> <NA>
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA027Q07NA PA027Q08NA
## 1: <NA> <NA>
## 2: Not checked Not checked
## 3: Not checked Not checked
## 4: <NA> <NA>
## 5: Not checked Not checked
## ---
## 3193: <NA> <NA>
## 3194: <NA> <NA>
## 3195: Not checked Not checked
## 3196: <NA> <NA>
## 3197: <NA> <NA>
## PA028Q01NA
## 1: <NA>
## 2: Private management and mainly public funding (e.g. <national example>)
## 3: Public management and mainly public funding (e.g. <national example>)
## 4: <NA>
## 5: Public management and mainly public funding (e.g. <national example>)
## ---
## 3193: <NA>
## 3194: <NA>
## 3195: Private management and mainly public funding (e.g. <national example>)
## 3196: <NA>
## 3197: <NA>
## PA029Q01NA
## 1: <NA>
## 2: 11-20 hours per week
## 3: 21-30 hours per week
## 4: <NA>
## 5: 21-30 hours per week
## ---
## 3193: <NA>
## 3194: <NA>
## 3195: up to 10 hours per week
## 3196: <NA>
## 3197: <NA>
## PA030Q01NA
## 1: <NA>
## 2: Most other children attended a <pre-primary education arrangement>.
## 3: <NA>
## 4: <NA>
## 5: Most other children attended a <pre-primary education arrangement>.
## ---
## 3193: <NA>
## 3194: <NA>
## 3195: Attendance was mandatory.
## 3196: <NA>
## 3197: <NA>
## PA032Q01TA PA032Q02TA PA032Q03TA PA032Q04TA PA032Q05TA PA033Q02TA
## 1: <NA> <NA> <NA> <NA> <NA> <NA>
## 2: No No No No No Agree
## 3: Yes No No No No Agree
## 4: <NA> <NA> <NA> <NA> <NA> <NA>
## 5: No No No No No Agree
## ---
## 3193: Yes Yes No No No Strongly agree
## 3194: No No No No No Strongly agree
## 3195: No No No No No Agree
## 3196: No No No No No Strongly agree
## 3197: <NA> <NA> <NA> <NA> <NA> <NA>
## PA033Q06TA PA033Q07TA PA033Q08TA PA033Q09TA
## 1: <NA> <NA> <NA> <NA>
## 2: Agree Disagree Disagree Agree
## 3: Agree Agree Agree Agree
## 4: <NA> <NA> <NA> <NA>
## 5: Agree Disagree Agree Disagree
## ---
## 3193: Agree Agree Agree Disagree
## 3194: Strongly agree Strongly agree Strongly agree Strongly agree
## 3195: Strongly agree Disagree Agree Disagree
## 3196: Strongly agree Strongly agree Strongly agree Strongly agree
## 3197: <NA> <NA> <NA> <NA>
## PA035Q01TA
## 1: <NA>
## 2: This is a serious concern for other people in my country but not me personally
## 3: This is a serious concern for me personally as well as others
## 4: <NA>
## 5: This is a serious concern for me personally as well as others
## ---
## 3193: This is a serious concern for me personally as well as others
## 3194: This is a serious concern for other people in my country but not me personally
## 3195: This is a serious concern only for people in other countries
## 3196: This is a serious concern for me personally as well as others
## 3197: <NA>
## PA035Q03TA
## 1: <NA>
## 2: This is a serious concern for other people in my country but not me personally
## 3: <NA>
## 4: <NA>
## 5: This is a serious concern only for people in other countries
## ---
## 3193: This is a serious concern for me personally as well as others
## 3194: This is a serious concern for other people in my country but not me personally
## 3195: This is a serious concern for other people in my country but not me personally
## 3196: This is a serious concern for me personally as well as others
## 3197: <NA>
## PA035Q04TA
## 1: <NA>
## 2: This is a serious concern for other people in my country but not me personally
## 3: This is a serious concern for me personally as well as others
## 4: <NA>
## 5: This is a serious concern for me personally as well as others
## ---
## 3193: This is a serious concern for me personally as well as others
## 3194: This is a serious concern for other people in my country but not me personally
## 3195: This is a serious concern only for people in other countries
## 3196: This is a serious concern for me personally as well as others
## 3197: <NA>
## PA035Q05TA
## 1: <NA>
## 2: This is a serious concern for me personally as well as others
## 3: This is a serious concern for me personally as well as others
## 4: <NA>
## 5: This is a serious concern only for people in other countries
## ---
## 3193: This is a serious concern for me personally as well as others
## 3194: This is a serious concern for other people in my country but not me personally
## 3195: This is a serious concern for me personally as well as others
## 3196: This is a serious concern for me personally as well as others
## 3197: <NA>
## PA035Q06TA
## 1: <NA>
## 2: This is a serious concern for me personally as well as others
## 3: This is a serious concern for me personally as well as others
## 4: <NA>
## 5: This is a serious concern for me personally as well as others
## ---
## 3193: This is a serious concern for me personally as well as others
## 3194: This is a serious concern for other people in my country but not me personally
## 3195: This is a serious concern for me personally as well as others
## 3196: This is a serious concern for me personally as well as others
## 3197: <NA>
## PA035Q07NA
## 1: <NA>
## 2: This is a serious concern for me personally as well as others
## 3: This is a serious concern for me personally as well as others
## 4: <NA>
## 5: This is not a serious concern for anyone
## ---
## 3193: This is a serious concern for me personally as well as others
## 3194: This is a serious concern for other people in my country but not me personally
## 3195: This is a serious concern for me personally as well as others
## 3196: This is a serious concern for me personally as well as others
## 3197: <NA>
## PA035Q08NA
## 1: <NA>
## 2: This is a serious concern for other people in my country but not me personally
## 3: This is a serious concern for me personally as well as others
## 4: <NA>
## 5: This is a serious concern for other people in my country but not me personally
## ---
## 3193: This is a serious concern for me personally as well as others
## 3194: This is a serious concern for other people in my country but not me personally
## 3195: This is a serious concern for me personally as well as others
## 3196: This is a serious concern for me personally as well as others
## 3197: <NA>
## PA036Q01TA PA036Q03TA PA036Q04TA
## 1: <NA> <NA> <NA>
## 2: Stay about the same Get worse Stay about the same
## 3: Stay about the same Stay about the same Get worse
## 4: <NA> <NA> <NA>
## 5: Improve Stay about the same Stay about the same
## ---
## 3193: Get worse Get worse Get worse
## 3194: Get worse Get worse Get worse
## 3195: Stay about the same Get worse Stay about the same
## 3196: Get worse Get worse Get worse
## 3197: <NA> <NA> <NA>
## PA036Q05TA PA036Q06TA PA036Q07NA
## 1: <NA> <NA> <NA>
## 2: Stay about the same Stay about the same Get worse
## 3: Get worse Get worse Get worse
## 4: <NA> <NA> <NA>
## 5: Get worse Get worse Stay about the same
## ---
## 3193: Get worse Get worse Get worse
## 3194: Get worse Get worse Get worse
## 3195: Get worse Stay about the same Get worse
## 3196: Get worse Get worse Get worse
## 3197: <NA> <NA> <NA>
## PA036Q08NA PA039Q01TA PA039Q02TA PA039Q03TA
## 1: <NA> <NA> <NA> <NA>
## 2: Stay about the same Country of test Country of test Country of test
## 3: Get worse Country of test Country of test Country of test
## 4: <NA> <NA> <NA> <NA>
## 5: Stay about the same Country of test Country of test Country of test
## ---
## 3193: Get worse Country of test Country of test Country of test
## 3194: Get worse Country of test Other country Country of test
## 3195: Get worse Country of test Country of test Country of test
## 3196: Get worse Country of test <NA> <NA>
## 3197: <NA> <NA> <NA> <NA>
## PA039Q04TA PA039Q05TA PA039Q06TA
## 1: <NA> <NA> <NA>
## 2: Country of test Country of test Country of test
## 3: Country of test Country of test Country of test
## 4: <NA> <NA> <NA>
## 5: Country of test Country of test Country of test
## ---
## 3193: Country of test Country of test Country of test
## 3194: Country of test Other country Other country
## 3195: Country of test Country of test Country of test
## 3196: <NA> <NA> <NA>
## 3197: <NA> <NA> <NA>
## PA041Q01TA PA042Q01TA AGE
## 1: <NA> <NA> 16.25
## 2: <NA> <NA> 15.50
## 3: Nothing <NA> 15.42
## 4: <NA> <NA> 15.83
## 5: <$Y or more but less than $Z> <NA> 16.08
## ---
## 3193: <$W or more but less than $X> <NA> 16.08
## 3194: <$W or more but less than $X> < $C > or more but less than < $D> 15.92
## 3195: <$W or more but less than $X> < $A > or more but less than < $B > 15.83
## 3196: <$X or more but less than $Y> <NA> 16.08
## 3197: <NA> <NA> 16.17
## PROGN
## 1: Germany: Lower secondary, access to upper secondary; academic education (exclusively students of the same track)
## 2: Germany: Lower secondary, access to upper secondary; academic education (exclusively students of the same track)
## 3: Germany: Lower secondary, access to upper secondary; academic education (exclusively students of the same track)
## 4: Germany: Lower secondary, access to upper secondary; academic education (exclusively students of the same track)
## 5: Germany: Lower secondary, access to upper secondary; academic education (exclusively students of the same track)
## ---
## 3193: Germany: Lower secondary comprehensive, achievement-based access to upper secondary (within school streaming)
## 3194: Germany: Lower secondary comprehensive, achievement-based access to upper secondary (within school streaming)
## 3195: Germany: Lower secondary comprehensive, achievement-based access to upper secondary (within school streaming)
## 3196: Germany: Lower secondary comprehensive, achievement-based access to upper secondary (within school streaming)
## 3197: Germany: Lower secondary comprehensive, achievement-based access to upper secondary (within school streaming)
## ISCEDL ISCEDD ISCEDO DISCLISCI TEACHSUP IBTEACH TDTEACH ENVAWARE
## 1: ISCED level 2 A General -0.2336 -0.8040 -0.6081 -0.8671 -0.5365
## 2: ISCED level 2 A General 0.2829 0.4879 -0.1573 -0.6849 -0.8048
## 3: ISCED level 2 A General 0.7001 1.4475 0.9882 0.5252 0.1710
## 4: ISCED level 2 A General 0.0039 0.5683 0.2087 -0.0742 -0.2342
## 5: ISCED level 2 A General 0.7634 -0.4496 0.5354 -0.0057 -0.4788
## ---
## 3193: ISCED level 2 A General 1.3185 0.7131 -0.0716 0.1910 0.4969
## 3194: ISCED level 2 A General 0.9373 0.9209 1.0631 2.0781 -0.4608
## 3195: ISCED level 2 A General 0.9327 -0.2618 -1.0496 -1.5358 1.3400
## 3196: ISCED level 2 A General -0.2683 -1.0505 0.3632 -0.0141 2.2983
## 3197: ISCED level 2 A General 0.0039 -0.8613 1.2163 0.5064 0.7295
## ENVOPT JOYSCIE INTBRSCI INSTSCIE SCIEEFF EPIST SCIEACT BSMJ GRADE
## 1: 0.1023 -0.8208 -0.5502 NA NA NA NA NA 0
## 2: 0.1023 -2.1154 -1.1289 -1.9301 -2.8303 -0.6856 -1.7518 42 0
## 3: 0.6937 -1.7159 -0.2246 -0.7182 -2.0922 0.5157 -1.7518 NA 0
## 4: -0.7797 -0.8208 -0.0831 -0.8262 -0.4258 0.8557 -0.1766 NA 1
## 5: -0.2211 0.6126 0.1976 -0.3035 -0.7130 0.3250 -0.7064 NA 1
## ---
## 3193: -1.7932 0.8225 0.9248 1.0218 -0.5699 -0.9398 0.5137 66 1
## 3194: 0.1798 1.3396 -0.0908 -0.5375 -0.7482 -0.7764 0.1676 26 1
## 3195: -0.2615 -1.6457 -0.5234 -0.9572 1.7590 -1.0683 0.2347 45 1
## 3196: 0.9402 -0.1808 -0.6723 -1.0839 0.9718 0.4023 0.4320 48 1
## 3197: 0.2069 0.1287 1.1786 1.7359 0.5407 2.1552 0.0289 71 1
## IMMIG MISCED FISCED HISCED HOMESCH ENTUSE
## 1: Native <NA> ISCED 5B ISCED 5B -0.5712 -0.4097
## 2: Native ISCED 3A, ISCED 4 ISCED 2 ISCED 3A, ISCED 4 -0.8913 -0.7527
## 3: Native ISCED 5A, 6 ISCED 2 ISCED 5A, 6 0.3373 0.4811
## 4: Native ISCED 5A, 6 ISCED 5A, 6 ISCED 5A, 6 0.0873 0.6122
## 5: Native ISCED 3A, ISCED 4 ISCED 2 ISCED 3A, ISCED 4 -0.2224 -0.0919
## ---
## 3193: Native ISCED 2 ISCED 2 ISCED 2 -0.4958 -1.1809
## 3194: Native ISCED 2 ISCED 3B, C ISCED 3B, C -0.3600 -0.6209
## 3195: Native ISCED 2 ISCED 2 ISCED 2 -0.4687 -0.4946
## 3196: Native ISCED 5B ISCED 5B ISCED 5B 0.2532 0.4857
## 3197: Native ISCED 3A, ISCED 4 ISCED 5A, 6 ISCED 5A, 6 0.0373 0.2874
## BMMJ1 BFMJ2 hisei REPEAT
## 1: 28 33 33 Repeated a <grade>
## 2: 43 28 43 Did not repeat a <grade>
## 3: 62 61 62 Did not repeat a <grade>
## 4: 55 88 88 Did not repeat a <grade>
## 5: 60 24 60 Did not repeat a <grade>
## ---
## 3193: 45 29 45 Did not repeat a <grade>
## 3194: 25 14 25 Did not repeat a <grade>
## 3195: 62 60 62 Did not repeat a <grade>
## 3196: 28 26 28 Did not repeat a <grade>
## 3197: 50 32 50 Did not repeat a <grade>
## DURECEC OUTHOURS MMINS
## 1: Attended ECEC for at least three but less than four years 6 180
## 2: Attended ECEC for at least three but less than four years 12 180
## 3: Attended ECEC for at least three but less than four years 9 360
## 4: Attended ECEC for at least three but less than four years 5 180
## 5: Attended ECEC for at least three but less than four years 5 180
## ---
## 3193: Attended ECEC for at least two but less than three years 12 360
## 3194: Attended ECEC for at least three but less than four years 16 180
## 3195: Attended ECEC for at least four but less than five years 2 180
## 3196: Attended ECEC for at least three but less than four years 7 180
## 3197: Attended ECEC for at least two but less than three years 18 180
## LMINS SMINS TMINS BELONG ANXTEST MOTIVAT COOPERATE CPSVALUE EMOSUPS
## 1: 180 405 1350 0.0171 -0.0345 -2.3950 -0.2882 0.1444 -1.5266
## 2: 180 270 1620 0.6642 -0.5792 -1.0795 1.0205 0.4713 1.0991
## 3: 360 540 NA 1.5043 -0.4331 0.4631 2.2879 -0.1160 1.0991
## 4: 180 0 1485 1.5043 -0.3318 -0.1756 -0.2882 -0.2024 -0.2495
## 5: 180 270 1620 -0.9909 -0.3165 -0.4495 -0.3352 -0.8356 0.1004
## ---
## 3193: 360 540 2700 0.6642 0.2594 0.7050 0.3950 -0.5546 1.0991
## 3194: 180 270 1440 2.5915 -0.2039 -0.8771 2.2879 2.1017 1.0991
## 3195: 180 270 1440 -0.6691 0.5348 -1.4136 -1.1664 -1.2734 -2.0584
## 3196: 180 90 2700 0.2947 1.3418 -1.6781 -0.6180 -1.5663 1.0991
## 3197: 180 180 2025 -1.1565 0.8565 -0.3240 1.0205 -0.8356 1.0991
## PERFEED ADINST SCCHANGE CHANGE SADDINST HADDINST ADDSCIIN
## 1: -1.5255 0.0437 no change 0 NA NA NA
## 2: -1.0450 -0.8141 one change 1 2 9 NA
## 3: 0.0933 0.0601 no change 0 3 6 NA
## 4: 0.0134 0.3530 no change 0 5 9 NA
## 5: -1.1537 -0.4268 no change 0 0 0 NA
## ---
## 3193: -0.5356 0.6524 <NA> NA NA NA NA
## 3194: 0.0134 0.3530 two or more changes 2 0 0 NA
## 3195: -0.4831 -1.2428 one change 1 2 8 NA
## 3196: -0.7766 -1.9656 two or more changes 2 1 2 NA
## 3197: 0.0134 0.9784 no change 0 1 1 NA
## COMSCSUP COMSCSTRLE COMSCSTRCO COMSCTSREL COMMASUP COMMASTRLE COMMASTRCO
## 1: NA NA NA NA NA NA NA
## 2: NA NA NA NA NA NA NA
## 3: NA NA NA NA NA NA NA
## 4: NA NA NA NA NA NA NA
## 5: NA NA NA NA NA NA NA
## ---
## 3193: NA NA NA NA NA NA NA
## 3194: NA NA NA NA NA NA NA
## 3195: NA NA NA NA NA NA NA
## 3196: NA NA NA NA NA NA NA
## 3197: NA NA NA NA NA NA NA
## COMMATSREL USESCH INTICT COMPICT AUTICT SOIAICT ICTHOME ICTSCH PRESUPP
## 1: NA -1.6678 0.3266 -0.7110 0.5553 -0.6455 NA NA NA
## 2: NA -0.2338 0.6555 -0.7320 -1.2688 -1.0602 NA NA -3.1278
## 3: NA -0.4210 0.3961 -1.6468 -1.7750 -1.0722 NA NA -1.6388
## 4: NA -0.0341 1.7671 -0.8290 -0.5628 -1.0906 NA NA NA
## 5: NA -0.8849 -0.4025 -1.4877 -0.4607 -0.2160 NA NA 0.3317
## ---
## 3193: NA -0.3951 NA NA NA NA NA NA -0.2612
## 3194: NA -0.2858 -1.1085 -0.8237 -0.7496 -1.2871 NA NA -0.0090
## 3195: NA -0.4210 -0.8470 -0.2270 0.3663 -0.7726 NA NA 0.2374
## 3196: NA -0.6274 2.6352 0.8348 1.2841 1.5243 NA NA -0.2796
## 3197: NA -0.4795 2.6352 1.9359 2.0955 1.1420 NA NA NA
## CURSUPP EMOSUPP PQSCHOOL PASCHPOL PQGENSCI PQENPERC PQENVOPT
## 1: NA NA NA NA NA NA NA
## 2: -0.6814 -0.9094 0.3349 -0.5840 -1.3540 -1.0072 0.6113
## 3: 0.1073 0.7454 0.6209 -0.6194 -0.3603 0.7367 -0.0220
## 4: NA NA NA NA NA NA NA
## 5: -0.0009 0.7454 -0.2730 -1.1510 -1.1637 -1.7747 0.8175
## ---
## 3193: -0.0665 0.1104 -1.5836 -1.4572 -0.1515 0.9201 -1.2620
## 3194: -0.2511 0.7454 -2.8162 -3.0421 1.8963 -1.5887 -1.2620
## 3195: -0.0586 -2.2251 -1.7183 -1.0951 -0.7357 -1.2877 0.1809
## 3196: 0.7781 0.7454 0.0121 -1.2768 1.8963 0.9201 -1.2620
## 3197: NA NA NA NA NA NA NA
## unfairteacher PARED COBN_F
## 1: 9 15 Germany
## 2: 8 13 Germany
## 3: 6 18 Germany
## 4: 8 18 Germany
## 5: 12 13 Germany
## ---
## 3193: 8 10 Germany
## 3194: 7 13 Another country (DEU)
## 3195: 14 10 Germany
## 3196: 14 15 Another country (DEU)
## 3197: 16 18 Germany
## COBN_M COBN_S LANGN
## 1: Another country (DEU) Germany German
## 2: Germany Germany German
## 3: Germany Germany German
## 4: Germany Germany German
## 5: Germany Germany German
## ---
## 3193: Germany Germany German
## 3194: Germany Germany German
## 3195: Germany Germany German
## 3196: Germany Germany German
## 3197: One of the former USSR republics Germany German
## OCOD1
## 1: Shop sales assistants
## 2: General office clerks
## 3: Real estate agents and property managers
## 4: Administrative and executive secretaries
## 5: Insurance representatives
## ---
## 3193: Medical assistants
## 3194: Cooks
## 3195: Regulatory government associate professionals
## 3196: Personal service workers
## 3197: Accounting and bookkeeping clerks
## OCOD2
## 1: Stock clerks
## 2: Shop sales assistants
## 3: Trade brokers
## 4: Dentists
## 5: Stonemasons, stone cutters, splitters and carvers
## ---
## 3193: Plumbers and pipe fitters
## 3194: Cleaners and helpers in offices, hotels and other establishments
## 3195: Insurance representatives
## 3196: Heavy truck and lorry drivers
## 3197: Agricultural and industrial machinery mechanics and repairers
## OCOD3 CULTPOSS HEDRES HOMEPOS ICTRES WEALTH
## 1: Not Applicable 2.1655 1.1563 1.5012 0.7936 1.0228
## 2: Travel consultants and clerks -0.7273 1.1563 -0.4854 -0.3461 -0.2249
## 3: Not Applicable -0.8341 -0.7218 0.3423 0.4892 1.3528
## 4: Not Applicable 1.0478 1.1563 0.9076 0.5970 0.4445
## 5: Not Applicable -0.0971 1.1563 -0.0510 -0.8588 -0.7139
## ---
## 3193: Health professionals 0.3941 1.1563 0.4752 -0.3461 -0.2635
## 3194: Child care workers -0.3560 -0.1108 0.3836 0.6932 1.2341
## 3195: Ambulance workers -1.6287 -1.9024 -0.7929 0.0614 0.1861
## 3196: Nursing associate professionals 0.3005 1.1563 0.8117 0.4091 0.4818
## 3197: Building architects 0.2737 -0.1108 0.7642 -0.0687 0.7604
## ESCS W_FSTUWT W_FSTURWT1 W_FSTURWT2 W_FSTURWT3 W_FSTURWT4 W_FSTURWT5
## 1: 0.3306 94.71886 47.35943 47.35943 142.0783 142.0783 142.0783
## 2: -0.4779 94.71886 47.35943 47.35943 142.0783 142.0783 142.0783
## 3: 0.9317 94.71886 47.35943 47.35943 142.0783 142.0783 142.0783
## 4: 1.6767 102.45550 51.62257 53.15760 152.6793 152.6793 152.6793
## 5: 0.0330 102.45550 51.62257 53.15760 152.6793 152.6793 152.6793
## ---
## 3193: -0.5113 91.98511 136.68920 139.74070 136.6892 136.6892 136.6892
## 3194: -0.5305 91.98511 136.68920 139.74070 136.6892 136.6892 136.6892
## 3195: -0.6260 91.98511 136.68920 139.74070 136.6892 136.6892 136.6892
## 3196: -0.0261 91.98511 136.68920 139.74070 136.6892 136.6892 136.6892
## 3197: 0.8388 91.98511 136.68920 139.74070 136.6892 136.6892 136.6892
## W_FSTURWT6 W_FSTURWT7 W_FSTURWT8 W_FSTURWT9 W_FSTURWT10 W_FSTURWT11
## 1: 142.07830 47.35943 142.0783 47.35943 142.07830 47.35943
## 2: 142.07830 47.35943 142.0783 47.35943 142.07830 47.35943
## 3: 142.07830 47.35943 142.0783 47.35943 142.07830 47.35943
## 4: 150.83360 53.15760 150.8336 53.15760 150.83360 51.62257
## 5: 150.83360 53.15760 150.8336 53.15760 150.83360 51.62257
## ---
## 3193: 45.51079 46.44085 139.7407 45.51079 46.44085 139.74070
## 3194: 45.51079 46.44085 139.7407 45.51079 46.44085 139.74070
## 3195: 45.51079 46.44085 139.7407 45.51079 46.44085 139.74070
## 3196: 45.51079 46.44085 139.7407 45.51079 46.44085 139.74070
## 3197: 45.51079 46.44085 139.7407 45.51079 46.44085 139.74070
## W_FSTURWT12 W_FSTURWT13 W_FSTURWT14 W_FSTURWT15 W_FSTURWT16 W_FSTURWT17
## 1: 47.35943 47.35943 47.35943 142.07830 142.07830 47.35943
## 2: 47.35943 47.35943 47.35943 142.07830 142.07830 47.35943
## 3: 47.35943 47.35943 47.35943 142.07830 142.07830 47.35943
## 4: 51.62257 51.62257 53.15760 152.67930 150.83360 53.15760
## 5: 51.62257 51.62257 53.15760 152.67930 150.83360 53.15760
## ---
## 3193: 136.68920 45.51079 46.44085 46.44085 46.44085 139.74070
## 3194: 136.68920 45.51079 46.44085 46.44085 46.44085 139.74070
## 3195: 136.68920 45.51079 46.44085 46.44085 46.44085 139.74070
## 3196: 136.68920 45.51079 46.44085 46.44085 46.44085 139.74070
## 3197: 136.68920 45.51079 46.44085 46.44085 46.44085 139.74070
## W_FSTURWT18 W_FSTURWT19 W_FSTURWT20 W_FSTURWT21 W_FSTURWT22 W_FSTURWT23
## 1: 142.07830 142.0783 47.35943 47.35943 47.35943 142.07830
## 2: 142.07830 142.0783 47.35943 47.35943 47.35943 142.07830
## 3: 142.07830 142.0783 47.35943 47.35943 47.35943 142.07830
## 4: 152.67930 150.8336 51.62257 51.62257 53.15760 152.67930
## 5: 152.67930 150.8336 51.62257 51.62257 53.15760 152.67930
## ---
## 3193: 45.51079 139.7407 45.51079 46.44085 45.51079 46.44085
## 3194: 45.51079 139.7407 45.51079 46.44085 45.51079 46.44085
## 3195: 45.51079 139.7407 45.51079 46.44085 45.51079 46.44085
## 3196: 45.51079 139.7407 45.51079 46.44085 45.51079 46.44085
## 3197: 45.51079 139.7407 45.51079 46.44085 45.51079 46.44085
## W_FSTURWT24 W_FSTURWT25 W_FSTURWT26 W_FSTURWT27 W_FSTURWT28 W_FSTURWT29
## 1: 142.07830 142.07830 142.0783 47.35943 142.07830 47.35943
## 2: 142.07830 142.07830 142.0783 47.35943 142.07830 47.35943
## 3: 142.07830 142.07830 142.0783 47.35943 142.07830 47.35943
## 4: 152.67930 152.67930 150.8336 53.15760 150.83360 53.15760
## 5: 152.67930 152.67930 150.8336 53.15760 150.83360 53.15760
## ---
## 3193: 46.44085 46.44085 139.7407 136.68920 45.51079 139.74070
## 3194: 46.44085 46.44085 139.7407 136.68920 45.51079 139.74070
## 3195: 46.44085 46.44085 139.7407 136.68920 45.51079 139.74070
## 3196: 46.44085 46.44085 139.7407 136.68920 45.51079 139.74070
## 3197: 46.44085 46.44085 139.7407 136.68920 45.51079 139.74070
## W_FSTURWT30 W_FSTURWT31 W_FSTURWT32 W_FSTURWT33 W_FSTURWT34 W_FSTURWT35
## 1: 142.0783 47.35943 47.35943 47.35943 47.35943 142.0783
## 2: 142.0783 47.35943 47.35943 47.35943 47.35943 142.0783
## 3: 142.0783 47.35943 47.35943 47.35943 47.35943 142.0783
## 4: 150.8336 51.62257 51.62257 51.62257 53.15760 152.6793
## 5: 150.8336 51.62257 51.62257 51.62257 53.15760 152.6793
## ---
## 3193: 136.6892 45.51079 46.44085 139.74070 136.68920 136.6892
## 3194: 136.6892 45.51079 46.44085 139.74070 136.68920 136.6892
## 3195: 136.6892 45.51079 46.44085 139.74070 136.68920 136.6892
## 3196: 136.6892 45.51079 46.44085 139.74070 136.68920 136.6892
## 3197: 136.6892 45.51079 46.44085 139.74070 136.68920 136.6892
## W_FSTURWT36 W_FSTURWT37 W_FSTURWT38 W_FSTURWT39 W_FSTURWT40 W_FSTURWT41
## 1: 142.0783 47.35943 142.0783 142.07830 47.35943 47.35943
## 2: 142.0783 47.35943 142.0783 142.07830 47.35943 47.35943
## 3: 142.0783 47.35943 142.0783 142.07830 47.35943 47.35943
## 4: 150.8336 53.15760 152.6793 150.83360 51.62257 51.62257
## 5: 150.8336 53.15760 152.6793 150.83360 51.62257 51.62257
## ---
## 3193: 136.6892 45.51079 139.7407 45.51079 139.74070 136.68920
## 3194: 136.6892 45.51079 139.7407 45.51079 139.74070 136.68920
## 3195: 136.6892 45.51079 139.7407 45.51079 139.74070 136.68920
## 3196: 136.6892 45.51079 139.7407 45.51079 139.74070 136.68920
## 3197: 136.6892 45.51079 139.7407 45.51079 139.74070 136.68920
## W_FSTURWT42 W_FSTURWT43 W_FSTURWT44 W_FSTURWT45 W_FSTURWT46 W_FSTURWT47
## 1: 47.35943 142.0783 142.0783 142.0783 142.07830 47.35943
## 2: 47.35943 142.0783 142.0783 142.0783 142.07830 47.35943
## 3: 47.35943 142.0783 142.0783 142.0783 142.07830 47.35943
## 4: 53.15760 152.6793 152.6793 152.6793 150.83360 53.15760
## 5: 53.15760 152.6793 152.6793 152.6793 150.83360 53.15760
## ---
## 3193: 139.74070 136.6892 136.6892 136.6892 45.51079 46.44085
## 3194: 139.74070 136.6892 136.6892 136.6892 45.51079 46.44085
## 3195: 139.74070 136.6892 136.6892 136.6892 45.51079 46.44085
## 3196: 139.74070 136.6892 136.6892 136.6892 45.51079 46.44085
## 3197: 139.74070 136.6892 136.6892 136.6892 45.51079 46.44085
## W_FSTURWT48 W_FSTURWT49 W_FSTURWT50 W_FSTURWT51 W_FSTURWT52 W_FSTURWT53
## 1: 142.0783 47.35943 142.07830 47.35943 47.35943 47.35943
## 2: 142.0783 47.35943 142.07830 47.35943 47.35943 47.35943
## 3: 142.0783 47.35943 142.07830 47.35943 47.35943 47.35943
## 4: 150.8336 53.15760 150.83360 51.62257 51.62257 51.62257
## 5: 150.8336 53.15760 150.83360 51.62257 51.62257 51.62257
## ---
## 3193: 139.7407 45.51079 46.44085 139.74070 136.68920 45.51079
## 3194: 139.7407 45.51079 46.44085 139.74070 136.68920 45.51079
## 3195: 139.7407 45.51079 46.44085 139.74070 136.68920 45.51079
## 3196: 139.7407 45.51079 46.44085 139.74070 136.68920 45.51079
## 3197: 139.7407 45.51079 46.44085 139.74070 136.68920 45.51079
## W_FSTURWT54 W_FSTURWT55 W_FSTURWT56 W_FSTURWT57 W_FSTURWT58 W_FSTURWT59
## 1: 47.35943 142.07830 142.07830 47.35943 142.07830 142.0783
## 2: 47.35943 142.07830 142.07830 47.35943 142.07830 142.0783
## 3: 47.35943 142.07830 142.07830 47.35943 142.07830 142.0783
## 4: 53.15760 152.67930 150.83360 53.15760 152.67930 150.8336
## 5: 53.15760 152.67930 150.83360 53.15760 152.67930 150.8336
## ---
## 3193: 46.44085 46.44085 46.44085 139.74070 45.51079 139.7407
## 3194: 46.44085 46.44085 46.44085 139.74070 45.51079 139.7407
## 3195: 46.44085 46.44085 46.44085 139.74070 45.51079 139.7407
## 3196: 46.44085 46.44085 46.44085 139.74070 45.51079 139.7407
## 3197: 46.44085 46.44085 46.44085 139.74070 45.51079 139.7407
## W_FSTURWT60 W_FSTURWT61 W_FSTURWT62 W_FSTURWT63 W_FSTURWT64 W_FSTURWT65
## 1: 47.35943 47.35943 47.35943 142.07830 142.07830 142.07830
## 2: 47.35943 47.35943 47.35943 142.07830 142.07830 142.07830
## 3: 47.35943 47.35943 47.35943 142.07830 142.07830 142.07830
## 4: 51.62257 51.62257 53.15760 152.67930 152.67930 152.67930
## 5: 51.62257 51.62257 53.15760 152.67930 152.67930 152.67930
## ---
## 3193: 45.51079 46.44085 45.51079 46.44085 46.44085 46.44085
## 3194: 45.51079 46.44085 45.51079 46.44085 46.44085 46.44085
## 3195: 45.51079 46.44085 45.51079 46.44085 46.44085 46.44085
## 3196: 45.51079 46.44085 45.51079 46.44085 46.44085 46.44085
## 3197: 45.51079 46.44085 45.51079 46.44085 46.44085 46.44085
## W_FSTURWT66 W_FSTURWT67 W_FSTURWT68 W_FSTURWT69 W_FSTURWT70 W_FSTURWT71
## 1: 142.0783 47.35943 142.07830 47.35943 142.0783 47.35943
## 2: 142.0783 47.35943 142.07830 47.35943 142.0783 47.35943
## 3: 142.0783 47.35943 142.07830 47.35943 142.0783 47.35943
## 4: 150.8336 53.15760 150.83360 53.15760 150.8336 51.62257
## 5: 150.8336 53.15760 150.83360 53.15760 150.8336 51.62257
## ---
## 3193: 139.7407 136.68920 45.51079 139.74070 136.6892 45.51079
## 3194: 139.7407 136.68920 45.51079 139.74070 136.6892 45.51079
## 3195: 139.7407 136.68920 45.51079 139.74070 136.6892 45.51079
## 3196: 139.7407 136.68920 45.51079 139.74070 136.6892 45.51079
## 3197: 139.7407 136.68920 45.51079 139.74070 136.6892 45.51079
## W_FSTURWT72 W_FSTURWT73 W_FSTURWT74 W_FSTURWT75 W_FSTURWT76 W_FSTURWT77
## 1: 47.35943 47.35943 47.35943 142.0783 142.0783 47.35943
## 2: 47.35943 47.35943 47.35943 142.0783 142.0783 47.35943
## 3: 47.35943 47.35943 47.35943 142.0783 142.0783 47.35943
## 4: 51.62257 51.62257 53.15760 152.6793 150.8336 53.15760
## 5: 51.62257 51.62257 53.15760 152.6793 150.8336 53.15760
## ---
## 3193: 46.44085 139.74070 136.68920 136.6892 136.6892 45.51079
## 3194: 46.44085 139.74070 136.68920 136.6892 136.6892 45.51079
## 3195: 46.44085 139.74070 136.68920 136.6892 136.6892 45.51079
## 3196: 46.44085 139.74070 136.68920 136.6892 136.6892 45.51079
## 3197: 46.44085 139.74070 136.68920 136.6892 136.6892 45.51079
## W_FSTURWT78 W_FSTURWT79 W_FSTURWT80 UNIT WVARSTRR
## 1: 142.0783 142.07830 47.35943 final variance unit 2 76
## 2: 142.0783 142.07830 47.35943 final variance unit 2 76
## 3: 142.0783 142.07830 47.35943 final variance unit 2 76
## 4: 152.6793 150.83360 51.62257 final variance unit 2 76
## 5: 152.6793 150.83360 51.62257 final variance unit 2 76
## ---
## 3193: 139.7407 45.51079 139.74070 final variance unit 1 1
## 3194: 139.7407 45.51079 139.74070 final variance unit 1 1
## 3195: 139.7407 45.51079 139.74070 final variance unit 1 1
## 3196: 139.7407 45.51079 139.74070 final variance unit 1 1
## 3197: 139.7407 45.51079 139.74070 final variance unit 1 1
## PV1MATH PV2MATH PV3MATH PV4MATH PV5MATH PV6MATH PV7MATH PV8MATH PV9MATH
## 1: 471.469 433.091 478.145 484.535 509.322 512.629 478.540 465.075 521.418
## 2: 516.694 409.077 432.485 447.409 525.874 502.626 458.893 463.910 445.486
## 3: 595.801 620.434 630.390 661.464 602.072 569.159 552.163 576.496 555.388
## 4: 464.959 565.340 546.584 551.572 537.498 530.947 562.206 567.884 508.543
## 5: 544.310 601.055 549.590 557.509 565.706 536.137 527.026 553.822 508.409
## ---
## 3193: 574.924 605.614 559.850 564.389 634.468 599.524 550.830 581.604 580.090
## 3194: 466.676 488.476 503.313 465.909 455.704 485.869 390.392 466.786 492.069
## 3195: 481.228 516.015 455.952 529.213 499.952 485.485 524.239 471.843 512.693
## 3196: 484.930 569.295 451.395 474.341 405.751 442.047 398.160 504.601 393.980
## 3197: 453.889 457.048 450.982 420.268 457.789 402.964 434.073 373.907 507.135
## PV10MATH PV1READ PV2READ PV3READ PV4READ PV5READ PV6READ PV7READ PV8READ
## 1: 521.211 502.003 496.498 565.270 483.306 526.473 571.484 504.256 540.133
## 2: 445.532 487.315 506.952 467.286 514.270 530.564 551.389 507.908 522.084
## 3: 595.139 662.796 669.937 712.614 676.133 655.724 611.293 570.889 639.288
## 4: 485.894 571.859 617.071 642.228 629.897 615.486 664.547 680.693 635.283
## 5: 551.410 502.153 609.559 457.285 573.686 603.908 600.070 532.382 547.934
## ---
## 3193: 610.191 565.173 589.183 545.499 526.549 594.841 599.656 564.761 605.990
## 3194: 483.722 468.543 500.846 483.114 481.048 470.344 502.570 472.789 484.206
## 3195: 515.443 469.214 497.507 473.499 565.520 541.649 483.492 577.923 469.681
## 3196: 498.056 514.441 555.882 438.760 577.504 556.342 511.922 535.354 518.674
## 3197: 521.085 473.345 490.194 453.271 467.869 484.351 496.412 466.983 423.968
## PV9READ PV10READ PV1SCIE PV2SCIE PV3SCIE PV4SCIE PV5SCIE PV6SCIE PV7SCIE
## 1: 542.179 560.011 456.902 438.004 545.892 479.152 534.589 480.263 536.195
## 2: 588.696 489.149 505.239 496.490 481.493 483.617 487.581 507.067 469.419
## 3: 653.044 724.243 566.428 573.291 605.697 596.867 563.718 590.518 557.647
## 4: 623.691 680.139 502.316 559.056 567.788 558.837 560.699 554.882 595.206
## 5: 546.370 517.088 546.052 551.288 585.787 554.661 580.118 559.199 545.550
## ---
## 3193: 596.397 631.526 642.547 602.784 612.358 559.039 633.847 643.724 574.740
## 3194: 473.132 462.372 462.247 460.016 477.083 471.114 465.200 466.343 453.367
## 3195: 494.513 520.552 465.048 540.221 460.978 518.033 495.812 467.595 565.344
## 3196: 416.386 531.113 510.979 548.805 506.677 543.929 494.295 501.528 538.732
## 3197: 451.623 501.428 479.236 476.725 493.800 456.413 447.858 431.566 445.621
## PV8SCIE PV9SCIE PV10SCIE PV1SCEP PV2SCEP PV3SCEP PV4SCEP PV5SCEP PV6SCEP
## 1: 464.027 541.845 551.729 511.466 502.423 475.693 518.713 592.368 528.154
## 2: 528.069 447.494 472.618 458.181 478.992 485.819 429.703 497.461 429.062
## 3: 578.396 588.283 576.174 515.727 562.030 549.460 538.292 548.910 532.647
## 4: 556.903 568.702 522.576 559.426 501.254 562.084 473.153 546.513 553.799
## 5: 586.696 519.772 538.775 547.676 532.270 561.320 463.304 537.240 548.220
## ---
## 3193: 609.382 586.694 620.580 653.032 583.725 570.678 587.251 533.071 587.347
## 3194: 453.375 493.701 492.639 559.561 506.976 468.344 482.271 538.393 498.655
## 3195: 442.223 475.526 482.813 551.680 504.918 517.353 553.283 441.574 532.672
## 3196: 519.336 504.437 513.425 553.313 505.058 560.123 531.482 528.185 477.048
## 3197: 416.007 488.881 474.947 476.728 492.336 470.786 476.805 469.611 464.454
## PV7SCEP PV8SCEP PV9SCEP PV10SCEP PV1SCED PV2SCED PV3SCED PV4SCED PV5SCED
## 1: 515.300 518.458 453.544 494.859 554.012 509.930 534.783 552.525 621.201
## 2: 505.891 504.342 560.448 480.020 524.697 515.923 502.114 512.534 523.950
## 3: 570.466 621.923 565.085 555.018 566.351 581.836 563.602 553.363 526.480
## 4: 552.285 549.551 582.430 522.017 607.946 527.629 580.106 494.238 566.784
## 5: 563.037 508.751 564.796 552.325 649.122 614.135 657.992 576.735 584.267
## ---
## 3193: 671.880 548.459 582.301 570.244 641.879 637.407 590.416 615.795 614.598
## 3194: 529.629 512.731 495.750 544.002 448.869 458.751 401.213 500.799 524.820
## 3195: 488.685 550.127 451.345 556.797 474.191 494.869 469.815 495.007 464.220
## 3196: 533.382 521.433 509.473 526.961 513.231 509.394 496.834 556.466 564.974
## 3197: 490.299 514.824 490.493 456.371 429.104 466.210 414.329 527.033 487.034
## PV6SCED PV7SCED PV8SCED PV9SCED PV10SCED PV1SCID PV2SCID PV3SCID PV4SCID
## 1: 511.885 529.636 586.984 489.215 577.563 503.105 500.639 467.085 513.940
## 2: 454.407 569.635 573.781 577.623 525.542 530.770 502.422 550.576 528.486
## 3: 595.490 576.972 639.832 611.890 552.712 534.630 604.790 547.097 557.023
## 4: 599.574 575.351 607.167 589.766 536.909 594.585 563.989 581.284 464.846
## 5: 617.811 613.911 606.751 610.775 699.580 592.376 570.833 549.657 544.071
## ---
## 3193: 603.622 673.494 560.256 621.028 588.443 613.682 608.998 552.519 599.806
## 3194: 512.058 431.888 439.159 405.975 491.533 464.907 479.382 438.467 476.415
## 3195: 511.208 499.441 502.721 388.677 558.401 498.056 536.459 491.422 527.909
## 3196: 491.181 540.432 532.684 508.429 539.952 468.915 478.354 477.014 526.633
## 3197: 468.273 518.858 486.320 459.884 430.763 378.416 498.510 392.653 428.619
## PV5SCID PV6SCID PV7SCID PV8SCID PV9SCID PV10SCID PV1SKCO PV2SKCO PV3SKCO
## 1: 572.853 511.661 511.147 530.869 420.499 525.487 536.572 543.306 508.493
## 2: 508.313 481.899 555.016 570.108 562.018 556.380 497.475 486.921 461.571
## 3: 551.802 560.004 597.838 641.167 610.791 552.798 600.282 565.166 553.559
## 4: 595.743 591.163 635.995 603.334 611.023 562.271 532.465 532.056 572.909
## 5: 556.435 564.809 599.003 541.340 596.860 580.646 524.830 593.957 570.505
## ---
## 3193: 590.229 607.175 659.579 550.655 602.715 530.822 645.447 625.133 549.410
## 3194: 487.128 425.480 514.925 501.506 444.241 488.197 503.125 511.844 469.682
## 3195: 430.063 540.295 507.010 542.739 458.691 543.035 470.865 461.423 499.517
## 3196: 505.620 466.684 476.709 547.622 495.517 488.194 568.623 555.900 546.604
## 3197: 418.326 435.227 451.680 447.013 465.804 425.325 490.683 453.160 473.240
## PV4SKCO PV5SKCO PV6SKCO PV7SKCO PV8SKCO PV9SKCO PV10SKCO PV1SKPE PV2SKPE
## 1: 490.286 437.696 477.196 507.278 467.605 515.512 473.464 541.963 553.448
## 2: 528.434 429.679 478.502 469.696 454.217 457.975 471.050 497.574 509.577
## 3: 579.229 609.308 561.027 611.243 535.003 589.938 583.380 591.829 576.560
## 4: 497.556 493.248 476.353 512.378 498.864 489.904 529.944 505.595 564.459
## 5: 560.844 501.846 539.959 514.480 533.800 559.101 610.134 578.684 607.295
## ---
## 3193: 608.496 560.145 615.635 648.844 628.501 563.165 652.049 643.574 615.618
## 3194: 510.264 530.790 485.132 492.961 490.315 463.087 534.269 441.862 439.312
## 3195: 477.861 417.012 437.089 452.354 501.028 452.174 481.964 486.278 487.945
## 3196: 586.305 540.930 494.787 528.498 563.168 512.523 558.586 546.703 522.497
## 3197: 492.769 452.542 466.986 446.486 464.195 443.000 547.519 458.496 377.026
## PV3SKPE PV4SKPE PV5SKPE PV6SKPE PV7SKPE PV8SKPE PV9SKPE PV10SKPE PV1SSPH
## 1: 507.480 532.879 492.459 503.294 540.777 475.915 561.557 505.842 525.426
## 2: 549.869 550.918 503.068 514.687 505.935 521.457 496.892 505.798 498.500
## 3: 547.610 579.692 622.797 625.389 623.122 584.205 607.029 640.021 601.757
## 4: 565.627 523.211 564.545 565.149 545.055 558.172 547.863 555.345 581.584
## 5: 594.884 616.365 556.785 566.987 548.954 595.943 593.590 601.215 579.246
## ---
## 3193: 567.827 589.863 554.035 575.435 626.355 581.377 594.388 609.728 614.319
## 3194: 495.906 476.379 451.901 462.685 473.419 471.096 446.178 455.809 433.281
## 3195: 555.099 425.594 452.599 476.304 477.839 480.650 470.261 519.494 500.432
## 3196: 504.258 565.715 510.397 520.550 485.728 504.861 502.375 474.333 507.518
## 3197: 430.395 480.491 442.679 472.644 414.736 410.383 463.828 495.938 488.404
## PV2SSPH PV3SSPH PV4SSPH PV5SSPH PV6SSPH PV7SSPH PV8SSPH PV9SSPH PV10SSPH
## 1: 512.134 523.128 611.146 508.269 551.374 416.859 523.044 519.577 511.461
## 2: 475.757 442.594 505.965 477.883 444.443 498.645 424.467 458.719 409.577
## 3: 560.042 596.354 562.569 567.467 565.444 560.480 591.085 557.592 505.805
## 4: 569.810 550.000 585.721 555.223 576.684 519.607 581.644 565.688 551.478
## 5: 616.823 612.351 592.663 614.627 614.457 611.019 597.431 627.540 647.663
## ---
## 3193: 611.378 577.384 620.686 627.139 558.871 670.397 592.947 556.802 627.678
## 3194: 488.874 407.251 457.484 462.448 447.679 471.522 491.419 552.217 482.668
## 3195: 529.908 457.578 451.837 476.183 476.267 431.263 498.842 459.896 465.711
## 3196: 506.717 498.221 448.471 530.716 504.428 483.568 476.469 487.320 480.907
## 3197: 473.791 432.351 464.737 408.796 439.681 461.396 392.871 437.124 456.191
## PV1SSLI PV2SSLI PV3SSLI PV4SSLI PV5SSLI PV6SSLI PV7SSLI PV8SSLI PV9SSLI
## 1: 521.651 537.710 510.922 587.292 492.602 528.213 430.859 531.610 516.416
## 2: 542.020 478.719 461.717 506.276 497.478 489.081 520.996 467.326 427.862
## 3: 559.741 588.303 563.789 586.021 562.624 590.676 553.712 540.019 584.200
## 4: 599.781 563.713 571.059 549.868 501.227 568.876 535.555 569.010 523.729
## 5: 541.462 607.407 578.817 575.471 529.124 552.320 565.654 478.199 592.747
## ---
## 3193: 610.180 605.320 600.276 580.106 615.268 578.643 634.545 651.211 560.845
## 3194: 490.266 440.704 487.583 450.842 536.020 456.181 450.958 480.892 516.542
## 3195: 529.721 551.118 486.739 527.697 566.181 515.970 446.769 486.421 462.921
## 3196: 511.888 540.565 551.855 523.931 503.698 526.677 544.697 565.964 529.979
## 3197: 448.114 453.436 470.345 440.248 387.409 427.414 496.271 409.607 415.160
## PV10SSLI PV1SSES PV2SSES PV3SSES PV4SSES PV5SSES PV6SSES PV7SSES PV8SSES
## 1: 491.594 512.654 510.956 492.811 550.701 468.539 499.703 445.586 518.914
## 2: 447.531 504.437 507.769 439.706 513.214 497.142 456.583 515.783 432.630
## 3: 536.087 605.421 591.910 563.616 590.108 588.362 602.514 532.261 542.795
## 4: 555.175 585.305 558.026 552.973 541.945 541.455 555.335 517.293 531.152
## 5: 597.970 562.204 575.104 581.441 560.578 555.000 571.226 566.072 533.328
## ---
## 3193: 645.547 589.850 630.844 596.367 625.576 593.585 623.862 638.028 615.175
## 3194: 514.458 471.670 563.569 439.659 512.396 510.050 485.940 428.223 489.456
## 3195: 551.828 453.414 549.236 521.930 486.107 502.460 487.532 447.816 551.896
## 3196: 534.788 484.255 498.800 552.386 525.242 470.891 507.746 486.418 539.775
## 3197: 453.747 476.050 497.970 499.658 480.599 409.286 453.592 511.962 412.686
## PV9SSES PV10SSES SENWT VER_DAT
## 1: 458.889 510.986 0.63838 15NOV16:14:44:40
## 2: 407.364 444.321 0.63838 15NOV16:14:44:40
## 3: 647.386 555.328 0.63838 15NOV16:14:44:40
## 4: 506.714 565.169 0.69052 15NOV16:14:44:40
## 5: 556.711 653.884 0.69052 15NOV16:14:44:40
## ---
## 3193: 618.419 645.697 0.61995 15NOV16:14:44:40
## 3194: 649.440 539.028 0.61995 15NOV16:14:44:40
## 3195: 429.580 518.244 0.61995 15NOV16:14:44:40
## 3196: 481.877 523.803 0.61995 15NOV16:14:44:40
## 3197: 478.462 465.851 0.61995 15NOV16:14:44:40- How many students are there in Germany?
pisa[CNTRYID == "Germany" & ST004D01T == "Female",
.N]## [1] 3197- The .N function returns the length of a vector/ number of rows. Use chaining with the .N function to answer Exercise 2
pisa[CNTRYID == "Germany" & ST004D01T == "Female"
][,.N]## [1] 3197General Learnings Using j we can: - select columns - summarize variables by performing actions on the variables - and create new variables.
pisa[,
.(CNTRYID)] #.() is a shorthand for list()## CNTRYID
## 1: Australia
## 2: Australia
## 3: Australia
## 4: Australia
## 5: Australia
## ---
## 158057: B-S-J-G (China)
## 158058: B-S-J-G (China)
## 158059: B-S-J-G (China)
## 158060: B-S-J-G (China)
## 158061: B-S-J-G (China)4.4.1 Exercises
First, create a function to convert dichotomous variable to numeric scoring x a character vector containing “Yes” and “No” responses
bin.to.num <- function(x){
if(is.na(x)) NA
else if (x == "Yes") 1L
else if (x == "No") 0L #L is to make sure variable is treated as interger
}Then use this function to create some variables as well as recoding gender
pisa[, `:=` (female = ifelse(ST004D01T == "Female", 1, 0),
sex = ST004D01T, #the := adds the variables directly to the DT
# At my house we have ...
desk = sapply(ST011Q01TA, bin.to.num),
own.room = sapply(ST011Q02TA, bin.to.num),
quiet.study = sapply(ST011Q03TA, bin.to.num),
computer = sapply(ST011Q04TA, bin.to.num),
software = sapply(ST011Q05TA, bin.to.num),
internet = sapply(ST011Q06TA, bin.to.num),
lit = sapply(ST011Q07TA, bin.to.num),
poetry = sapply(ST011Q08TA, bin.to.num),
art = sapply(ST011Q09TA, bin.to.num),
book.sch = sapply(ST011Q10TA, bin.to.num),
tech.book = sapply(ST011Q11TA, bin.to.num),
dict = sapply(ST011Q12TA, bin.to.num),
art.book = sapply(ST011Q16NA, bin.to.num))]Then create new variables by combining preexisiting ones
pisa[, `:=`
(math = rowMeans(pisa[, c(paste0("PV", 1:10, "MATH"))], na.rm = TRUE),
reading = rowMeans(pisa[, c(paste0("PV", 1:10, "READ"))], na.rm = TRUE),
science = rowMeans(pisa[, c(paste0("PV", 1:10, "SCIE"))], na.rm = TRUE))]- The computer and software variables that were created above ask a student whether they had a computer in their home that they can use for school work (computer) and whether they had educational software in their home (software). Find the proportion of students in Germany and Uruguay that have a computer in their home or have educational software.
pisa[CNTRYID %in% c("Germany", "Uruguay"),
table(computer, software)]## software
## computer 0 1
## 0 745 101
## 1 5280 4669#Adding labels/ make it look prettier
pisa[CNTRYID %in% c("Germany", "Uruguay"),
.(computer = factor(ST011Q04TA, levels = c("No", "Yes")),
software = factor(ST011Q05TA, levels = c("No", "Yes")))
][,
table(computer, software)
]## software
## computer No Yes
## No 745 101
## Yes 5280 4669The total number of students in Germany and Uruguay that have a computer in their home OR have educational software is 101 + 5280 + 4669 = 10050; proportion = 10050/10795 = .93
- For just female students, find the proportion of students who have their own room (own.room) or a quiet place to study (quiet.study).
pisa[sex == "Female",
table(own.room, quiet.study)]## quiet.study
## own.room 0 1
## 0 4928 10680
## 1 5828 51172The total number of female students who have their own room OR a quiet place to study is 5828 + 10680 + 51172 = 67680; proportion = 67680/ 72608 = .93
4.5.1 Exercises
- Calculate the proportion of students who have art in their home (art) and the average age (AGE) of the students by gender.
pisa[,
.(art = mean(art, na.rm = TRUE),
AGE = mean(AGE, na.rm = TRUE)),
by = .(sex)]## sex art AGE
## 1: Female 0.5806813 15.81553
## 2: Male 0.5624181 15.81395- Within a by argument you can discretize a variable to create a grouping variable. Perform a median split for age within the by argument and assess whether there are age difference associated with having your own room (own.room) or a desk (desk).
pisa[,
.(own.room = mean(own.room, na.rm = TRUE),
desk = mean(desk, na.rm = TRUE)),
by =.(age = (AGE < median(AGE)))
]## age own.room desk
## 1: FALSE 0.7962661 0.8187880
## 2: TRUE 0.7993508 0.8385361#Older students (above median age) are more likely to have their own room and desks4.8 Lab
This afternoon when we discuss supervised learning, we’ll ask you to develop some models to predict the response to the question. Do you expect your child will go into a ?” (PA032Q03TA).
- Recode this variable so that a “Yes” is 1 and a “No” is a -1. Save the variable as sci_car
pisa[,
"sci_car" := sapply(PA032Q03TA,
function(x){
if(is.na(x)) NA
else if (x == "Yes") 1L
else if (x == "No") -1L
})
][,
table(sci_car)]## sci_car
## -1 1
## 17680 11575- Calculate descriptives for this variable by sex and country. Specifically, the proportion of test takers whose parents said “Yes” or 1.
pisa[,
.(sci_car = mean(sci_car, na.rm =TRUE)),
by = .(sex = sex, country = CNTRYID)]## sex country sci_car
## 1: Female Australia NaN
## 2: Male Australia NaN
## 3: Male Brazil NaN
## 4: Female Brazil NaN
## 5: Female Canada NaN
## 6: Male Canada NaN
## 7: Female Colombia NaN
## 8: Male Colombia NaN
## 9: Female France -0.35842027
## 10: Male France -0.21721478
## 11: Female Germany -0.83480176
## 12: Male Germany -0.70852921
## 13: Female Israel NaN
## 14: Male Israel NaN
## 15: Female Italy -0.44499774
## 16: Male Italy -0.26525766
## 17: Female Japan NaN
## 18: Male Japan NaN
## 19: Male Jordan NaN
## 20: Female Jordan NaN
## 21: Female Korea -0.44214559
## 22: Male Korea -0.07555871
## 23: Female Lebanon NaN
## 24: Male Lebanon NaN
## 25: Male Mexico 0.39756098
## 26: Female Mexico 0.32005772
## 27: Female New Zealand NaN
## 28: Male New Zealand NaN
## 29: Male United States NaN
## 30: Female United States NaN
## 31: Female Uruguay NaN
## 32: Male Uruguay NaN
## 33: Female B-S-J-G (China) NaN
## 34: Male B-S-J-G (China) NaN
## sex country sci_carAfter you’ve done this, spend some time investigating the following variables Label and then do the following using data.table and/or sparklyr:
- Means and standard deviations (sd) for the variables that you think will be most predictive of sci_car
As this is a response by parents, variables that might be the most predictive of sci_car should be observable to parents: Student expected occupational status (BSMJ), Out of school study time (OUTHOURS), Home Educational Resources (HEDRES), Family Wealth (WEALTH)
pisa[,
.(ExpectedStatus = mean(BSMJ, na.rm = TRUE),
OutsideStudy = mean(OUTHOURS, na.rm = TRUE),
HomeRes = mean(HEDRES, na.rm = TRUE),
Wealth = mean(WEALTH, na.rm = TRUE))
]## ExpectedStatus OutsideStudy HomeRes Wealth
## 1: 61.72995 18.81952 -0.3267211 -0.3924522- Calculate these same descriptives by groups (by sci_car and by sex)
pisa[,
.(ExpectedStatus = mean(BSMJ, na.rm = TRUE),
OutsideStudy = mean(OUTHOURS, na.rm = TRUE),
HomeRes = mean(HEDRES, na.rm = TRUE),
Wealth = mean(WEALTH, na.rm = TRUE)),
by = .(sci_car, sex)
]## sci_car sex ExpectedStatus OutsideStudy HomeRes Wealth
## 1: NA Female 64.06874 18.50026 -0.36310480 -0.4382905
## 2: NA Male 60.52828 19.59710 -0.36207869 -0.3310930
## 3: 1 Female 67.39607 19.62531 -0.29527963 -0.7163693
## 4: -1 Female 55.41587 16.65803 -0.07037943 -0.3093473
## 5: -1 Male 53.34787 17.29241 -0.16207490 -0.2446372
## 6: 1 Male 63.96993 20.14187 -0.24663518 -0.5764796- Calculate correlations between these variables and sci_car
pisa[,
.(ExpectedStatus = cor(sci_car, BSMJ, use = "na.or.complete"),
OutsideStudy = cor(sci_car, OUTHOURS, use = "na.or.complete"),
HomeRes = cor(sci_car, HEDRES, use = "na.or.complete"),
Wealth = cor(sci_car, WEALTH, use = "na.or.complete"))]## ExpectedStatus OutsideStudy HomeRes Wealth
## 1: 0.3155443 0.1088173 -0.0744448 -0.1638986- Create new variables 6a. Discretize the math and reading variables using the OECD means (490 for math and 493) and code them as 1 (at or above the mean) and -1 (below the mean), but do in the data.table way without using the $ operator.
pisa[,
"mathfac" := sapply(math,
function(x){
if(is.na(x)) NA
else if (x >= 490) 1L
else if (x < 490) -1L
})]
pisa[,
"readfac" := sapply(reading,
function(x){
if(is.na(x)) NA
else if (x >= 493) 1L
else if (x < 493) -1L
})]6b. Calculate the correlation between these variables and the list of variables above.
pisa[,
.(ExpectedStatusM = cor(mathfac, BSMJ, use = "na.or.complete"),
OutsideStudyM = cor(mathfac, OUTHOURS, use = "na.or.complete"),
HomeResM = cor(mathfac, HEDRES, use = "na.or.complete"),
WealthM = cor(mathfac, WEALTH, use = "na.or.complete"),
ExpectedStatusR = cor(readfac, BSMJ, use = "na.or.complete"),
OutsideStudyR = cor(readfac, OUTHOURS, use = "na.or.complete"),
HomeResR = cor(readfac, HEDRES, use = "na.or.complete"),
WealthR = cor(readfac, WEALTH, use = "na.or.complete"))]## ExpectedStatusM OutsideStudyM HomeResM WealthM ExpectedStatusR
## 1: 0.069999 -0.07348207 0.2774099 0.2832184 0.1121201
## OutsideStudyR HomeResR WealthR
## 1: -0.09406116 0.2754228 0.2851246- Chain together a set of operations. For example, create an intermediate variable that is the average of JOYSCIE and INTBRSCI,and then calculate the mean by country by sci_car through chaining
pisa[,
"lovescience" := ((JOYSCIE + INTBRSCI)/2)
][,
.(sci_car = mean(sci_car, na.rm = TRUE)),
by = .(CNTRYID)]## CNTRYID sci_car
## 1: Australia NaN
## 2: Brazil NaN
## 3: Canada NaN
## 4: Colombia NaN
## 5: France -0.2908490
## 6: Germany -0.7779123
## 7: Israel NaN
## 8: Italy -0.3572836
## 9: Japan NaN
## 10: Jordan NaN
## 11: Korea -0.2517959
## 12: Lebanon NaN
## 13: Mexico 0.3577465
## 14: New Zealand NaN
## 15: United States NaN
## 16: Uruguay NaN
## 17: B-S-J-G (China) NaN- Transform variables, specifically recode MISCED and FISCED from characters to numeric variables.
ParentEdu = c('MISCED', 'FISCED')
pisa[,
(ParentEdu) := lapply(.SD, as.numeric), .SDcols = ParentEdu
][,
.(class(MISCED),
class(FISCED))]## Warning in lapply(.SD, as.numeric): NAs introduced by coercion
## Warning in lapply(.SD, as.numeric): NAs introduced by coercion## V1 V2
## 1: numeric numeric